Question-declaration coupling in a university meeting talk: Discourse of social inequality and collegiality

Conversation Analysis (CA) deals with the description of the microscopic and corpus-driven data in an ‘unmotivating looking’ analytical fashion. As long as there are new, interesting, or deviant features from the data, they are always worthy of a micro analysis. For this paper, we report the ‘question-declaration coupling’ in meeting talks as a new feature and explicate it through the discourse of social inequality and collegiality in the academe. The data came from a total of five recorded meetings from three departments, such as Education, Arts Science, and Social Work, in a private university in Manila, Philippines. The meetings lasted for five hours and 50 minutes. From adjacency pairs of question-answer, the sequential pattern shows that the questions deserve conspicuous answers from the subordinates, but the Chair automatically couples them with declarative sentences and other utterances that serve as continuers. The pattern is categorised as a strategic turn-suppressing mechanism to hold back the members from possibly challenging the existing policies of the institution. It is also seen as a strategic mechanism to deprive the members of extending the litanies of possible counter-arguments. From a positive perspective, we argue that it is through the air of social inequality and collegiality that people are able to know their boundaries in an ongoing interaction. Toward the end, we state the implications of the results for teaching and learning socio-pragmalinguistics. We also recommend future cross-linguistic comparisons for these microscopic features under study, considering the small corpus of this study.

The Prevalence of MRI-Defined Sacroiliitis and Classification of Spondyloarthritis in Patients with Acute Anterior Uveitis: A Longitudinal Single-Centre Cohort Study

Background: Acute anterior uveitis (AAU) is a relatively common extra-musculoskeletal manifestation of axial spondyloarthritis (axSpA); however, data on the prevalence of active sacroiliitis in patients with AAU are limited. Methods: 102 patients with AAU and 39 healthy subjects (HS) underwent clinical assessment and sacroiliac joint MRI. Patients with absence of active sacroiliitis were reassessed after two years. International Spondyloarthritis Society (ASAS) classification criteria for axSpA (regardless of patient’s age) and expert opinion for definitive diagnosis of axSpA were applied. Results: Although chronic back pain was equally present in both groups, bone marrow edema (BME) in SIJ and BME highly suggestive of axSpA was found in 52 (51%) and in 33 (32%) patients with AAU compared with 11 (28%) and none in HS, respectively. Out of all AAU patients, 41 (40%) patients fulfilled the ASAS classification criteria for axSpA, and 29 (28%) patients were considered highly suggestive of axSpA based on clinical features. Two out of the 55 sacroiliitis-negative patients developed active sacroiliitis at the two-year follow-up. Conclusions: One-third of patients with AAU had active inflammation on SIJ MRI and clinical diagnosis of axSpA. Therefore, patients with AAU, especially those with chronic back pain, should be referred to a rheumatologist, and the examination should be repeated if a new feature of SpA appears.

Diagnosis for Slight Bearing Fault in Induction Motor Based on Combination of Selective Features and Machine Learning

Induction motors are widely used in industry and are essential to industrial processes. The faults in motors lead to high repair costs and cause financial losses resulting from unexpected downtime. Early detection of faults in induction motors has become necessary and critical in reducing costs. Most motor faults are caused by bearing failure. Machine learning-based diagnostic methods are proposed in this study. These methods use effective features. First, load currents of healthy and faulty motors are measured while the rotating speed is changing continuously. Second, experiments revealed the relationship between the magnitude of the amplitude of specific signals and the rotating speed, and the rotating speed is treated as a new feature. Third, machine learning-based diagnoses are conducted. Finally, the effectiveness of machine learning-based diagnostic methods is verified using experimental data.

Machine learning approaches in Covid-19 severity risk prediction in Morocco

The purpose of this study is to develop and test machine learning-based models for COVID-19 severity prediction. COVID-19 test samples from 337 COVID-19 positive patients at Cheikh Zaid Hospital were grouped according to the severity of their illness. Ours is the first study to estimate illness severity by combining biological and non-biological data from patients with COVID-19. Moreover the use of ML for therapeutic purposes in Morocco is currently restricted, and ours is the first study to investigate the severity of COVID-19. When data analysis approaches were used to uncover patterns and essential characteristics in the data, C-reactive protein, platelets, and D-dimers were determined to be the most associated to COVID-19 severity prediction. In this research, many data reduction algorithms were used, and Machine Learning models were trained to predict the severity of sickness using patient data. A new feature engineering method based on topological data analysis called Uniform Manifold Approximation and Projection (UMAP) shown that it achieves better results. It has 100% accuracy, specificity, sensitivity, and ROC curve in conducting a prognostic prediction using different machine learning classifiers such as X_GBoost, AdaBoost, Random Forest, and ExtraTrees. The proposed approach aims to assist hospitals and medical facilities in determining who should be seen first and who has a higher priority for admission to the hospital.

Finite Element Simulation of a Crack Growth in the Presence of a Hole in the Vicinity of the Crack Trajectory

The aim of this paper was to present a numerical simulation of a crack growth path and associated stress intensity factors (SIFs) for linear elastic material. The influence of the holes’ position and pre-crack locations in the crack growth direction were investigated. For this purpose, ANSYS Mechanical R19.2 was introduced with the use of a new feature known as Separating Morphing and Adaptive Remeshing Technology (SMART) dependent on the Unstructured Mesh Method (UMM), which can reduce the meshing time from up to several days to a few minutes, eliminating long preprocessing sessions. The presence of a hole near a propagating crack causes a deviation in the crack path. If the hole is close enough to the crack path, the crack may stop at the edge of the hole, resulting in crack arrest. The present study was carried out for two geometries, namely a cracked plate with four holes and a plate with a circular hole, and an edge crack with different pre-crack locations. Under linear elastic fracture mechanics (LEFM), the maximum circumferential stress criterion is applied as a direction criterion. Depending on the position of the hole, the results reveal that the crack propagates in the direction of the hole due to the uneven stresses at the crack tip, which are consequences of the hole’s influence. The results of this modeling are validated in terms of crack growth trajectories and SIFs by several crack growth studies reported in the literature that show trustworthy results.

Residue–Residue Contact Can Be a Potential Feature for the Prediction of Lysine Crotonylation Sites

Lysine crotonylation (Kcr) is involved in plenty of activities in the human body. Various technologies have been developed for Kcr prediction. Sequence-based features are typically adopted in existing methods, in which only linearly neighboring amino acid composition was considered. However, modified Kcr sites are neighbored by not only the linear-neighboring amino acid but also those spatially surrounding residues around the target site. In this paper, we have used residue–residue contact as a new feature for Kcr prediction, in which features encoded with not only linearly surrounding residues but also those spatially nearby the target site. Then, the spatial-surrounding residue was used as a new scheme for feature encoding for the first time, named residue–residue composition (RRC) and residue–residue pair composition (RRPC), which were used in supervised learning classification for Kcr prediction. As the result suggests, RRC and RRPC have achieved the best performance of RRC at an accuracy of 0.77 and an area under curve (AUC) value of 0.78, RRPC at an accuracy of 0.74, and an AUC value of 0.80. In order to show that the spatial feature is of a competitively high significance as other sequence-based features, feature selection was carried on those sequence-based features together with feature RRPC. In addition, different ranges of the surrounding amino acid compositions’ radii were used for comparison of the performance. After result assessment, RRC and RRPC features have shown competitively outstanding performance as others or in some cases even around 0.20 higher in accuracy or 0.3 higher in AUC values compared with sequence-based features.

Starting 2022 with a new feature: full-text search for the Front Matter blog

Fresh into 2022, the Front Matter blog today is launching an important new feature: full-text search of all blog posts. An example query would be for reference manager:As the Front Matter blog has a lot of posts about reference managers, ...

Automated analysis of platelet microstructures using a feature length orientation space

The ability to measure elongated structures such as platelets and colonies, is an important step in the microstructural analysis of many materials. Widely used techniques and standards require extensive manual interaction making them slow, laborious, difficult to repeat and prone to human error. Automated approaches have been proposed but often fail when analysing complex microstructures. This paper addresses these challenges by proposing a new, automated image analysis technique, to reliably assess platelet microstructure. Tools from Mathematical Morphology are designed to probe the image and map the response onto a new feature-length orientation space (FLOS). This enables automated measurement of key microstructural features such as platelet width, orientation, globular volume fraction, and colony size. The method has a wide field of view, low dependency on input parameters, and does not require prior thresholding, common in other automated analysis techniques. Multiple datasets of complex Titanium alloys were used to evaluate the new techniques which are shown to match measurements from expert materials scientists using recognized standards, while drastically reducing measurement time and ensuring repeatability. The per-pixel measurement style of the technique also allows for the generation of useful colourmaps, that aid further analysis and provide evidence to increase user confidence in the quantitative measurements.