Miracle-dz/UniFAR-FTUs huggingface dataset:
Early Prediction of Sepsis Onset Using Neural Architecture Search Based on Genetic Algorithms

Sepsis is a life-threatening condition with a high mortality rate. Early prediction and treatment are the most effective strategies for increasing survival rates. This paper proposes a neural architecture search (NAS) model to predict the onset of sepsis with a low computational cost and high search performance by applying a genetic algorithm (GA). The proposed model shares the weights of all possible connection nodes internally within the neural network. Externally, the search cost is reduced through the weight-sharing effect between the genotypes of the GA. A predictive analysis was performed using the Medical Information Mart for Intensive Care III (MIMIC-III), a medical time-series dataset, with the primary objective of predicting sepsis onset 3 h before occurrence. In addition, experiments were conducted under various prediction times (0–12 h) for comparison. The proposed model exhibited an area under the receiver operating characteristic curve (AUROC) score of 0.94 (95% CI: 0.92–0.96) for 3 h, which is 0.31–0.26 higher than the scores obtained using the Sequential Organ Failure Assessment (SOFA), quick SOFA (qSOFA), and Simplified Acute Physiology Score (SAPS) II scoring systems. Furthermore, the proposed model exhibited a 12% improvement in the AUROC value over a simple model based on the long short-term memory neural network. Additionally, it is not only optimally searchable for sepsis onset prediction, but also outperforms conventional models that use similar predictive purposes and datasets. Notably, it is sufficiently robust to shape changes in the input data and has less structural dependence.
Supralaryngeal articulation across voicing and aspiration in Yemba vowels

Both prevoicing and aspiration are thought to affect supraglottal cavity size during and following the production of stop consonants. However, many languages implement stop laryngeal contrasts using both prevoicing and aspiration, making it difficult to independently link observed tongue position differences to one effect or the other. Yemba (Grassfields Bantu) offers a unique opportunity to separately study these two effects, as the language contains stop consonants which independently vary in prevoicing and aspiration. The current study examines tongue position in vowels following Yemba stop consonants using acoustic and ultrasound data. For-
Employing Atmospheric Sensors and Turbulent Energy Cascade Theory to Quantify Hazardous Airborne Transmissibility

Abstract Airborne viruses, bacteria, or toxins are dangerous because of the nature of the human transmission pathway through breathing. However, every airborne component must conform to the laws of physics governing atmospheric propagation. Given the fact that most atmospheric flows, at both ground level and throughout the atmosphere, are highly turbulent, the mechanisms of turbulence can be employed to understand the propagation of such components. In this paper, the problem of harmful airborne pathogen transmission is considered in the context of atmospheric turbulence and wall-bounded flow theory. Two approaches are considered: one of them relies on singular measurements of building boundary distances and morphology, and the other relies on constant temperature measurement. The theoretical and practical potential of these approaches is then discussed and explained in a larger urban context.
Knowledge Distillation for End-to-End Automatic Speech Recognition

With the development of deep learning, larger and deeper neural networks for automatic speech recognition (ASR) which yield excellent results can be trained more efficiently. Although increased model size leads to larger performance improvement, it also poses more challenges for real-life applications due to memory and latency requirements. This project proposes to use knowledge distillation (KD) to utilise Wav2vec 2.0, a large self-supervised model for various speech downstream tasks, to train smaller models, which are suitable for real-life applications and shows very good performance at the same time. As initial validation of the effectiveness of distilling knowledge from self-supervised ASR models, several distillation strategies for connectionist temporal classification (CTC) model are designed. Experiments on Librispeech clean-100h subset confirm a relative worderror-rate (WER) reduction of 17.7% and 16.4% on Librispeech clean and other test sets respectively compared to the model trained without KD. As a novel contribution, a distillation loss for transducer-based speech recognition models which is both memory efficient and effective is proposed. This method preserves full distribution and focuses on one-best alignment information, unlike existing methods that used full-lattice collapsed distribution. A relative WER reduction of 12.6% and 9.2% on Librispeech test-clean and test-other sets is observed in experiments with Librispeech clean-100h subset when compared to transducer models without KD, which is comparable against existing methods. Further experiments with labels of different qualities demonstrate the robustness of the proposed method. Experiments are also carried out on streaming transducer models. By introducing a time shift operation, preliminary KD results confirm a relative WER improvement of 7.4% and 6.8% on clean and other test sets respectively. Experiments are further extended to a semi-supervised learning scenario. By incorporating clean-360h and other-360h as extra untranscribed speech for KD, even larger WER reduction is achieved on test-clean and test-other sets, suggesting a promising approach of combining knowledge distillation with semi-supervised learning to improve ASR models.
IJQRM-11-2020-0362_proof 30..52

Purpose – This study examines whether certification improves the dimensions of total quality management (TQM) and whether the impact of certification is similar across companies of different sizes and industries. The benefits of certification for companies have been widely discussed in recent years. The general debate has been partly marked by the dispute about whether companies will benefit more from certification or the implementation of TQM. This debate has led to numerous studies on the benefits of certification; however, few studies simultaneously have examined traditional TQM issues and the requirements of the new quality standard, ISO9001: 2015, aswell as the updatedEuropean Foundation forQualityManagement (EFQM) criteria. Design/methodology/approach – This study was conducted via a survey of Finnish SMEs and covered both industrial and service companies. The study comprehensively compared industrial companies with service companies and small companies with medium-sized companies. Findings – In industrial and small enterprises, certification clearly has a positive effect on the dimensions of TQM, but a similar effect was not observed in medium-sized enterprises or in the service sector. Originality/value – This is one of the first studies to examine the effect of certification on TQM in different types of SMEswhile simultaneously consideringEFQMand ISO 9001:2015 in Finland. The significant originality of this research lies in the formation of a comprehensive research framework for the dimensions of TQM.
Contrastive Self-Supervised Learning for Tabular Data

Much of the worlds data is stored in databases or spreadsheets and consists of columns of features which comprise of continuous, categorical or binary variables. A range of challenging and important machine learning tasks rely on data of this format such as applications in healthcare, business analytics and recommendation systems. However, large amounts of labelled data for a specific task can often be either expensive or impossible to obtain. Self-supervised learning is a technique in machine learning in which representations are learned from large amounts of unlabelled data and then used to assist other models trained on tasks for which there is only a small amount of labelled data. Recent research has shown that this approach can improve performance on tabular data when labelled data is scarce, and therefore finding better self-supervised learning methods for tabular data is an important area of research. Contrastive learning is a framework for learning representations that has recently received a great deal of attention, particularly in computer vision, because of it’s state-of-the-art performance in self-supervised learning. However, whilst it has been applied successfully to image data, the vast majority of current contrastive learning methods require the use of bespoke augmentations that are only applicable to images. Tabular data is a domain where data augmentations are not as widely used because there do not exist strong inductive biases about the latent information in the inputs for all datasets that would be equivalent to, for example, translation invariance for images. There has therefore been almost no research into contrastive learning for tabular data, in spite of the fact that contrastive learning does not demand the use of augmentations, and could potentially be used to achieve equivalent improvements in performance for tabular data if adapted correctly. In this thesis, we explore the use of contrastive learning for tabular data and develop a novel approach called Masked Contrastive Learning (MCL) that does not require domainspecific augmentations and we show that it can be used to achieve state-of-the-art results for self-supervised learning on tabular data. We also extend our approach using a novel masking scheme we develop which allows the mask generator to learn to retain the structural informa-
The role of potassium channels on vasorelaxant effects of elabela in rat thoracic aorta

Background This study aims to investigate the roles of potassium channel subtypes in the vasorelaxant effect mechanism of elabela, which is a recently discovered endogenous apelin receptor ligand. Methods The vascular rings (4-mm) obtained from the thoracic aortas of 20 male Wistar Albino rats were placed into the isolated tissue bath system. The resting tension was set to 1 g. The aortic rings were contracted with 10-5 molar phenylephrine after the equilibration period (90 min). Elabela was applied cumulatively (10-10-10-6 molar) to the aortic rings in the plateau phase. The experimental protocol was repeated in the presence of specific potassium channel subtype inhibitors to determine the role of potassium channels in the vasorelaxant effect mechanism of elabela. Results Elabela induced a concentration-dependent vasorelaxation (p<0.001). The maximum relaxation level was approximately 51% according to phenylephrineinduced contraction. Vasorelaxant effect level of elabela statistically significantly decreased after removal of the endothelium (p<0.05). Tetraethylammonium (1 milimolar), 4-Aminopyridine (1 milimolar), glyburide (10 micromolar), and barium chloride (30 micromolar) statistically significantly decreased the vasorelaxant effect level of elabela (p<0.001, p<0.001, p<0.01, and p<0.05 respectively). However, anandamide (10 micromolar) and apamin (100 nanomolar) did not statistically significantly change the vasorelaxant effect level of elabela. Conclusion Our results suggest that large-conductance calciumactivated, voltage-gated, adenosine triphosphate-sensitive, and inward-rectifier potassium channels are involved in the vasorelaxant effect mechanism of elabela in the rat thoracic aorta.
Assessment of the current SARS-CoV-2 epidemiological situation in the EU / EEA , projections for the end-of-year festive season and strategies for response , 17 th update 24 November 2021 Summary

Increases in case notifications, hospitalisations and intensive care unit (ICU) admissions for SARS-CoV-2 have been observed in October and early November in the majority of EU/EEA countries, after a period of decline in August and September 2021. This has been driven by circulation of the Delta variant (B.1.617.2) in the context of insufficient vaccine uptake and widespread relaxation of non-pharmaceutical interventions (NPIs). Whilst the burden from COVID-19 is particularly high in a number of countries experiencing low vaccine uptake, there is evidence of rising burden even among countries with higher uptake. The current epidemiological situation is to a large part driven by the high transmissibility of the Delta variant that counteracts the reduction in transmission achieved by the current vaccination rollout in the EU/EEA.
COMPARISON OF THE ROTARY AND RECIPROCATING FILE SYSTEMS USING THE CBCT: AN ORGINAL STUDY

Introduction: In endodontics, the use of rotary file systems has made canal preparation easier and safer. There are numerous commercially available file systems that work with continuous and reciprocating motions. Aim: Using "cone beam computed tomography (CBCT)," we examined the "Canal transportation (CT), Canal centering ability (CA), touched (TS), and untouched surfaces (UT)" of dentin after instrumentation in various file systems. Materials and Procedures: With 100 removed human teeth, we launched an in vitro investigation. We compared continuous rotation ProTaper Gold (PG) and TruNatomy (TN) to reciprocating motion Reciproc blue (RB) and MicroMega One RECI (MR). These were sorted into four groups of equal size. The manufacturer's instructions were followed, and the teeth were prepared as directed. Before and after the preparations, CBCT imaging was performed on all of the teeth at lengths of "8 mm, 5 mm, and 2 mm" from the apex. For the CT, CA, TS, and US following instrumentation, we compared all of the groups pre and after operatively. Using the ANOVA statistical test, the obtained data was analysed for variation, with p0.05 being considered significant. Results: The CT, CA, TS, and US showed a significant difference at all levels from the apex for all of the groups. We found substantial differences in the CT and CA at all three levels from apex. MR revealed the least amount of CT and CA compared to other methods. At all three levels, MR revealed the least US and the most TS surface. The PG noticed a higher CA. Conclusion: PG exhibited better CA while MG showed lesser CT, and had better contact with the dentin surface which is essential for the removal of the infected canal surface. Hence it can be recommended for the clinical endodontic application.
Neuropsychological predictors of conversion to dementia and cognitive trajectory of older adults with mild cognitive impairment in a two-year follow-up study

Faria, Camila de Assis; Charchat-Fichman, Helenice (Advisor). Neuropsychological predictors of conversion to dementia and cognitive trajectory of older adults with mild cognitive impairment in a two-year follow-up study. Rio de Janeiro, 2016. 94p. Thesis – Psychology Department, Pontifícia Universidade Católica do Rio de Janeiro. This study aimed to determine the best neuropsychological predictors of dementia and analyze the cognitive trajectory of older adults with mild cognitive impairment (MCI) in a two-year follow-up study. 87 older adults with MCI were submitted to a broad neuropsychological battery and 62 were evaluated two years later. 21.8% converted to dementia. The results revealed that episodic memory and working memory are the best predictors of dementia from MCI after two years. Older adults showing decline in two or more cognitive functions showed greater deficits in functionality and higher percentages of conversion to dementia when compared to older adult with executive function decline. In addition, the results showed that depressive symptoms are specifically associated with older adults showing exclusively executive function decline.
swims (SWIM) and the 30-second crossbar jumps (30CJ) to a laboratory-based test of anaerobic power, the Wingate anaerobic power test (WAnT). Thirteen elite female water polo players

The purpose of the current study was to compare two water polo specific tests, the 14x25m swims (SWIM) and the 30-second crossbar jumps (30CJ) to a laboratory-based test of anaerobic power, the Wingate anaerobic power test (WAnT). Thirteen elite female water polo players (mean ± SD: age 22.0 ± 4.4 years, height 168.7 ± 7.9 cm, body mass 65.9 ± 6.1 kg, body fat 23.6 ± 3.5 %, maximum oxygen uptake 51.4 ± 4.5 ml∙kg-1∙min-1) participated in the study. The SWIM involved 14 repeated ‘all-out’ sprints every 30 seconds. Swimming time was recorded and sprint velocity, mean velocity (Vmean) and gradient of the linear regression equation (GRADIENT) were calculated. The 30CJ involved repeated in-water water polo jumps and touching the goal crossbar with both hands. The number of touches in 30 seconds was recorded. Additionally, the subjects completed a 30 second Wingate anaerobic power test and mean power (Mp) and fatigue index (FI) were calculated. Kendall tau (τ) rank correlation was used to examine for correlation between ranks. Significance level was set at P ≤ 0.05 No significant correlation was found between any of the measures of the WAnT and the two sport specific tests. It was suggested that WAnT may not be an appropriate evaluation tool for anaerobic power assessment of water polo players, stressing the importance of sport-specific tests.
The effect on the tool flank wear width VBmax during turning of Hybrid Mg/(8wt% SiC(p) +2wt% Al2O3(p) +1wt% Gr(p) MMC using PVD coated cermet tool

In this study Taguchi Optimization Technique was used, followed by ANOVA to evaluate the machinability i.e. turning of a hybrid Mg/(8wt%SiCp+2wt%Al2O3p+1wt%Grp) MMC using PVD coated cermet tool under the dry turning conditions. Several experiments were conducted using the L27 (313) orthogonal array on NH26 automated lather machine. ANOVA was used to determine the impression of the turning parameter on the VBmax of the tool flank. The finding of the research work investigated that the cutting speed in the dominant factor influence the tool wear width. The confirmation test revealed that the Taguchi technique has been successful in the optimization of the machining parameters for minimum tool wear. The optimal parameter arrangement for minimum tool wear width is at cutting speed of 120 m/min, with the feed rate of 0.4 mm/rev and depth of cut at 0.75 mm. The scanning electron microscope images revealed notches, groves and pits on the tool flank face that is clearly visible at different magnification units. Herein, this paper discuss about several reported synthesis techniques, various characterization techniques used for structural and surface properties identifications, different morphologies and various potential applications of transition metal oxide based nanocomposites.
Analysis of Effects of Change of Gear Parameter Module on Transmission Error in Spur Gear using Interference Volume Method

Gear drives are the most used elements in power transmission systems. Any defect in the gear of these drives leads to noise and vibrations, which affect power/torque transmission. Transmission Error (TE) is one of the critical causes that arises due to the tooth geometry error, cracks in gear, profile error etc. Our work demonstrates, the consequences occurring due to the changes in gear parameter module on TE for spur gear pair of 1:1 gear ratio with pitch error, using the interference volume method. The spur gear without pitch error and with pitch error having the involute profile of standard tooth dimension system and stub tooth dimension system are modelled and assembled in CAD software SOLIDWORKS. The interference volume values obtained from the gear pairs are recorded for one mesh cycle. Various graphs plotted between the angle of rotation and interference volume values for one mesh cycle. It is found from the analysis that as the module and pitch error increase TE also increases. The effect of variation of module and pitch error is more in the standard tooth dimension system than the stub tooth dimension system.
Fair Policy Learning

Ensuring machine learning algorithms deployed in the real world do not result in unexpected unfairness or social implications is becoming increasingly important. However, there exists a clear gap in literature for a measure of fairness that can detect discrimination against multiple sensitive attributes while also handling continuous or discrete outcomes. In this thesis, we propose a fairness measure, Fair-COCCO, based on the conditional cross-covariance operator on reproducing kernel Hilbert Spaces. This novel method generalise to the majority of existing fairness notions and naturally extends to settings with continuous outcomes and multidimensional sensitive attributes. Additionally, we demonstrate how the proposed measure can be readily implemented in stochastic gradient optimisation for fair policy learning in supervised learning settings. Empirical evaluations of Fair-COCCO on synthetic and realworld experiments reveal favourable comparisons to state-of-the-art techniques in balancing predictive power and fairness. We also see much potential in applying machine learning to analyse fairness in observed behaviour, especially in complex and high-dimensional real-world environments. To that end, we propose the first known definition of fairness for sequences of decisions and showcase how Fair-COCCO can be applied to quantify fairness in these problems. Building off these definitions, we turn to learning fair policies in real-world conditions, where learning is constrained to be performed offline. We propose Fair-PoLe, a novel inverse reinforcement learning that operates completely offline and is computationally efficient and functionally expressive when compared to existing methods. We illustrate the potential for Fair-PoLe to learn policies that balance imitation of expert policies with fair outcomes on the challenging problem of sepsis treatment.