Miracle-dz/UniFAR-FTUs huggingface dataset:
‘The Facts Formed a Line of Buoys in the Sea of My Own Imagination’: History, Fiction and Speculative Biography

Biographies that openly include conjecture and speculation have attracted virulent criticism for moving from a supposedly factual, historical mode of writing to a more imaginative and, therefore, (necessarily) non-historical approach. Profiling the most contentious of biographical sub-genres – the ‘speculative biography’, which proclaims the central role of authorial interpretation in biographical writing – this investigation uses a case study approach to focus on a number of rarely discussed works that illustrate varied aspects of the productive role of speculation in biographical writing. It also aims, by exploring how fiction and history can be framed and discussed in relation to biographical research and writing, to diffuse the limiting irreconcilability that is often mobilised when discussing these two terms as oppositional and antithetical tendencies, and instead suggests the rich potential of utilising the two as complementary writing strategies to produce biographies as rich, appealing and thought-provoking historically-informed narratives of real lives and experience. By referring to reviews, it also provides some contextualisation of how these biographies have been received by critics and reviewers. The focus is on international works.
Fixed-Point Theorem-Based Voltage Stability Margin Estimation Techniques for Distribution Systems With Renewables

The future distribution systems expose to an unprecedented level of uncertainties due to renewable resources, nontraditional loads, aging infrastructure, etc., posing potential risks to secure operation of the system.This article proposes a new technique to estimate the voltage stability margin of the distribution systems with high penetration of renewables.Its convergence and robustness under complex and stressed working conditions are guaranteed in theory. This technique is handy for the operation as it features self-adaptive step size and is applicable to general system topology. It leverages a newly derived analytical solvability certificate based on the Kantorovich fixed-point theorem. A fast version of the proposed technique is duly proposed to speed up the computation up to 8 times while maintaining high accuracy, which lends itself to online and time-sensitive emergency tasks. Numerical simulations with various IEEE test feeders verify the performance of the techniques.
Assessment of a novel marker of ICU strain, the ICU Activity Index, during the COVID-19 pandemic in Victoria, Australia

Objectives: To validate a real-time Intensive Care Unit (ICU) Activity Index as a marker of ICU strain from daily data available from the Critical Health Resource Information System (CHRIS), and to investigate the association between this Index and the need to transfer critically ill patients during the coronavirus disease 2019 (COVID-19) pandemic in Victoria, Australia. Design: Retrospective observational cohort study. Setting: All 45 hospitals with an ICU in Victoria, Australia. Participants: Patients in all Victorian ICUs and all critically ill patients transferred between Victorian hospitals from 27 June to 6 September 2020. Main outcome measure: Acute interhospital transfer of one or more critically ill patients per day from one site to an ICU in another hospital. Results: 150 patients were transported over 61 days from 29 hospitals (64%). ICU Activity Index scores were higher on days when critical care transfers occurred (median, 1.0 [IQR, 0.4–1.7] v 0.6 [IQR, 0.3–1.2]; P < 0.001). Transfers were more common on days of higher ICU occupancy, higher numbers of ventilated or COVID-19 patients, and when more critical care staff were unavailable. The highest ICU Activity Index scores were observed at hospitals in north-western Melbourne, where the COVID-19 disease burden was greatest. After adjusting for confounding factors, including occupancy and lack of available ICU staff, a rising ICU Activity Index score was associated with an increased risk of a critical care transfer (odds ratio, 4.10; 95% CI, 2.34–7.18; P < 0.001). Conclusions: The ICU Activity Index appeared to be a valid marker of ICU strain during the COVID-19 pandemic. It may be useful as a real-time clinical indicator of ICU activity and predict the need for redistribution of critical ill patients.
Modelling Maltese noun plural classes without morphemes

ABSTRACT Word-based models of morphology propose that complex words are stored without reference to morphemes. One of the questions that arises is whether information about word forms alone is enough to determine a noun's number from its form. We take up this question by modelling the classification and production of the Maltese noun plural system, using models that do not assume morphemic representations. We use the Tilburg Memory-Based Learner, a computational implementation of exemplar theory and the Naive Discriminative Learner, an implementation of Word and Paradigm, for classification. Both models classify Maltese nouns well. In their current implementations, TiMBL and NDL cannot concatenate sequences of phones that result in word forms. We used two neural networks architectures (LSTM and GRU) to model the production of plurals. We conclude that the Maltese noun plural system can be modelled on the basis of whole words without morphemes, supporting word-based models of morphology.
Thermal Ionization of Hydrogen in Hydrous Olivine With Enhanced and Anisotropic Conductivity

Trace amounts of hydrogen in olivine can significantly increase its conductivity. However, the conduction mechanism in hydrous olivine is still unclear, which hinders the in‐depth understanding of the high conductivity structures of the asthenosphere. We investigate the proton conduction mechanism in hydrous olivine using ab initio calculations. Several models were examined using climbing image nudged elastic band and ab initio molecular dynamics methods. We found that hydrogen trapped at the Mg (or Fe) defect is more mobile than hydrogen trapped at the Si defect. At high temperature, we observed the ionization of hydrogen from cation defects leading to high and anisotropic proton conductivity along the [100] direction. The highly anisotropic conductivity caused by thermal ionized hydrogen at high temperature explains the experimental observations on olivine single crystals.
The effect of guided discovery instructional strategy on grade nine learners' performance in chemical reactions in Mankweng circuit, South Africa

Teaching strategies play a vital role in improving learners’ performance. This study investigated the effect of Guided Discovery Instructional Strategy (GDIS) on Grade nine learners’ performance in chemical reactions and determined the effect of GDIS on gender. A quasi-experimental design was used with a sample comprised 75 Grade nine learners purposively selected from two schools in Mankweng Circuit based on Grade 12 poor performances. Learners were randomly assigned to the Experimental Group (EG) (N = 40) and Control Group (CG) (N = 35) taught for two weeks using GDIS for EG, and Talk and Chalk Method (TCM) for CG. The results show that there were statistically significant differences between CG post-test (T-test: t(78) = 19.89; p = 0.05); Cohen d = 4.40 for EG and CG T-test: t(68) = -1.03; p > 0.05; Cohen d = 0.2. EG taught using GDIS outperformed those using TCM (ANCOVA: F = 361.49, p = 0.05). GDIS did not discriminate against gender in EG because no statistically significant differences in performance after the intervention (Mann Whitney U-test: U = 178.50, p = 0.58), suggesting that GDIS improved all learners’ performance in chemistry, but not CTM. The findings provide teachers and stakeholders with empirical evidences on a strategy that improved learners’ performance. Also, GDIS did not discriminate against gender like the TCM, suggesting that the strategy encourages girls to study science, which contributes to narrowing the existing gender gap of male and female in Science, Technology, Engineering and Mathematics (STEM) subjects.
Barriers to implementation of distance learning during the COVID-19 outbreak: Teacher perspective

Since the outbreak of the COVID-19 pandemic in early 2020, distance learning has become one of the main educational issues globally. With the transition of all instruction to the online environment, teachers in Serbia have faced a number of challenges and barriers that have affected the quality of their work. In this paper, we wanted to analyse the barriers that teachers faced during the first months of distance learning. The research was conducted combining quantitative and qualitative analysis of data collected on a sample of 122 high school teachers from the Southeast Serbia (Nis, Leskovac). The results show that teachers recognise evaluation barriers as the predominant ones, followed by organisational-administrative ones, while the least represented were material-technical barriers to distance learning. The findings also confirm that material and technical barriers are most common among teachers with the longest work experience, as well as that organisational-administrative and socio-emotional barriers are the least common among teachers of vocational subjects. Although the focus of the paper was on the barriers in the implementation of distance learning, it can be concluded that teachers recognise certain benefits of this type of instruction and indicate that it can be used as a supplement to regular instruction. The main pedagogical implications of the paper refer to the empowerment of teachers through professional development in the field of distance learning, but also to the need to create new professional development programmes in this field which will enable the development of functional knowledge and relevant competencies for the immediate situational context of modern instruction.
The Resolved Mutual Information Function as a Structural Fingerprint of Biomolecular Sequences for Interpretable Machine Learning Classifiers

In the present article we propose the application of variants of the mutual information function as characteristic fingerprints of biomolecular sequences for classification analysis. In particular, we consider the resolved mutual information functions based on Shannon-, Rényi-, and Tsallis-entropy. In combination with interpretable machine learning classifier models based on generalized learning vector quantization, a powerful methodology for sequence classification is achieved which allows substantial knowledge extraction in addition to the high classification ability due to the model-inherent robustness. Any potential (slightly) inferior performance of the used classifier is compensated by the additional knowledge provided by interpretable models. This knowledge may assist the user in the analysis and understanding of the used data and considered task. After theoretical justification of the concepts, we demonstrate the approach for various example data sets covering different areas in biomolecular sequence analysis.
Cognitive reappraisal mitigates affective valence declines during exercise at the ventilatory threshold

ABSTRACT Affective valence during exercise is thought to switch from positive to negative at the ventilatory threshold (VT). The emotion regulation strategy cognitive reappraisal (CR) can be used to reevaluate emotional stimuli to reduce negative and increase positive emotional experience. We investigated if CR applied during endurance exercise at VT would increase self-reported positive affect and reduce self-reported arousal and perceived exertion during a 60-min run. 26 young recreational runners (n = 13 women, age 25.4 ± 4.3 years old; BMI 21.3 ± 2.3 kg/m2; peak oxygen consumption (VO2peak) = 55.02 ± 7.31 ml/kg/min, 58 ± 17 km/week) performed three sessions each separated by one week. On the first session, runners completed a maximal incremental test to determine their ventilatory threshold. During the next two sessions, runners completed a 60-min run at their VT on a treadmill while applying either CR or unregulated (UR) instructions in counterbalanced order. Participants were reminded of the emotion regulation instructions every five minutes and reported exertion, affective valence, and arousal every 10 min. Similar levels of exertion were reported during the CR and UR sessions. However, they reported marginally higher arousal during CR than UR (p=0.076). They also reported more positive valence during CR than UR (p<0.001). Post-hoc analysis showed that affective valence was higher during CR than UR (p<0.01) after 30, 40 and 50 min of exercise. During endurance exercise at the ventilatory threshold, cognitive reappraisal attenuates the decreases in affect after 30 min in recreational runners.
Attention to a Moment in Time Impairs Episodic Distinctiveness during Rapid Serial Visual Presentation

Human attention is limited in the ability to select and segregate relevant distinct events from the continuous flow of external information while concurrently encoding their temporal succession. While it is well-known that orienting attention to one external target stimulus impairs the encoding of ensuing relevant external events, it is still unknown whether orienting attention to internally generated events can interfere with concurrent processing of external input. We addressed this issue by asking participants to identify a single target embedded among distractors in a non-spatial rapid serial visual presentation (RSVP) stream and to indicate whether that target appeared before or after an internally estimated midpoint of the stream. The results indicate that (a) such an internally generated temporal benchmark does not interfere with the identification of a subsequent physical target stimulus but (b) the two events cannot be accurately segregated when the physical target immediately follows the internally generated temporal event. These findings indicate that the asymmetrical distribution around the midpoint of order reversals reflects an impaired temporal discrimination ability. Orienting attention to a moment in time reduces episodic distinctiveness as much as orienting attention to external events.
Melatonin Levels in Children with Obesity Are Associated with Metabolic Risk and Inflammatory Parameters

Melatonin, the hormone of circadian rhythm regulation, is involved in the modulation of mitochondrial activity through its antioxidant and anti-inflammatory properties. Alteration of circadian rhythms such as sleep is related to obesity and metabolic pathogenesis in adulthood, but studies during childhood are scarce. The present study investigated the association of melatonin with metabolic and inflammatory markers in children with (n = 113) and without obesity (n = 117). Melatonin was measured in saliva four and two hours before bedtime, and after one hour of sleep. Cardiometabolic factors, high sensitivity C-reactive protein, immune markers (monocyte chemoattractant protein-1, plasminogen activator inhibitor-1, tumor necrosis α and interferon-γ), leptin and ghrelin were determined. Sleep duration was recorded by a questionnaire. The melatonin level at 1 h after sleep was found to be increased more than twofold in children with obesity (90.16 (57.16–129.16) pg/mL) compared to controls (29.82 (19.05–61.54) pg/mL, p < 0.001) and was related to fat mass (rho = 0.294, p < 0.001); melatonin levels at 1 h after sleep were inversely correlated with high-density lipoprotein cholesterol. Positive correlation was found with apolipoprotein B, adipokines, high sensitivity C-reactive protein, plasminogen activator inhibitor-1 and tumor necrosis factor-α. Shorter sleep duration and earlier waking times were recorded in children with obesity. In conclusion, melatonin in children with obesity appears to be involved in the global metabolic and inflammatory alteration of this condition.
Corpus Callosum Atrophy in Detection of Mild and Moderate Alzheimer’s Disease Using Brain Magnetic Resonance Image Processing and Machine Learning Techniques

Background: The total number of people with dementia is projected to reach 82 million in 2030 and 152 in 2050. Early and accurate identification of the underlying causes of dementia, such as Alzheimer’s disease (AD) is of utmost importance. A large body of research has shown that imaging techniques are most promising technologies to improve subclinical and early diagnosis of dementia. Morphological changes, especially atrophy in various structures like cingulate gyri, caudate nucleus, hippocampus, frontotemporal lobe, etc., have been established as markers for AD. Being the largest white matter structure with a high demand of blood supply from several main arterial systems, anatomical alterations of the corpus callosum (CC) may serve as potential indication neurodegenerative disease. Objective: To detect mild and moderate AD using brain magnetic resonance image (MRI) processing and machine learning techniques. Methods: We have performed automatic detection and segmentation of the CC and calculated its morphological features to feed into a multivariate pattern analysis using support vector machine (SVM) learning techniques. Results: Our results using large patients’ cohort show CC atrophy-based features are capable of distinguishing healthy and mild/moderate AD patients. Our classifiers obtain more than 90%sensitivity and specificity in differentiating demented patients from healthy cohorts and importantly, achieved more than 90%sensitivity and > 80%specificity in detecting mild AD patients. Conclusion: Results from this analysis are encouraging and advocate development of an image analysis software package to detect dementia from brain MRI using morphological alterations of the CC.
Reputation-Based Truth Discovery With Long-Term Quality of Source in Internet of Things

Although the Internet of Things (IoT) devices have been widely used for data collection in various applications, the observed data of an object from each IoT device (i.e., source) may vary from the ground truth due to the different qualities of IoT devices and sensing environments. Truth discovery has become a promising technology to extract the truth among multiple conflicting pieces of data from different sources. Existing methods usually assume the quality of source (source reliability) is unknown a priori and will be estimated as the weight for calculating the truth during each truth discovery task. However, in a long-term data observation scenario, the quality of source can be accumulated and utilized in the future truth discovery process. Aiming to take the long-term quality of source into consideration, in this article, we propose a reputation-based truth discovery method to derive the truth from the conflicting data. Specifically, we propose a generalized formulation with linear constraint for the truth discovery problem, which can cope with different regulations on the source reliability. Instead of directly using weight as the source reliability, we also define the reliability of a source by its contribution to the loss function. Then, we propose a novel reputation model to quantify the newly defined source reliability, which will be accumulated as the long-term source quality. Finally, we propose a reputation-based truth discovery model, where initial weights are assigned based on source reputations. Experiments conducted on real-weather conditions and GPS data sets demonstrate that our reputation-based truth discovery can reduce the number of iterations during truth discovery and achieve high accuracy.
More than meets the eye: uncovering the evolution of the OECD’s institutional priorities in education

ABSTRACT Over the last 20 years, since the launch of its flagship study the Programme for International Student Assessment (PISA), the Organization for Economic Co-operation and Development (OECD) has become a behemoth of transnational influence on education policy making. To better understand the evolution of the OECD’s perspective on education and the importance of PISA within the OECD’s overall publications, I analysed over 900 OECD publications on education with a topic modelling approach. The analysis revealed that contrary to popular belief, the OECD is a multi-centric organization with various agendas in education policy making, focusing on economic changes, higher education, management, planning, and budgeting. PISA made up only 12% of the overall text corpus and was therefore far less discussed than expected. This is in sharp contrast to the general impression held, namely that the OECD’s work in education mostly relates to PISA. Therefore, this study reveals that the OECD has a far broader agenda than anticipated. This paper acts as a systematic review of OECD literature to gain insights into its institutional objectives.
Whole-exome sequencing prioritizes candidate genes for hereditary cataract in the Emory mouse mutant

Abstract The Emory cataract (Em) mouse mutant has long been proposed as an animal model for age-related or senile cataract in humans—a leading cause of visual impairment. However, the genetic defect(s) underlying the autosomal dominant Em phenotype remains elusive. Here, we confirmed development of the cataract phenotype in commercially available Em/J mice [but not ancestral Carworth Farms White (CFW) mice] at 6–8 months of age and undertook whole-exome sequencing of candidate genes for Em. Analysis of coding and splice-site variants did not identify any disease-causing/associated mutations in over 450 genes known to underlie inherited and age-related forms of cataract and other lens disorders in humans and mice, including genes for lens crystallins, membrane/cytoskeleton proteins, DNA/RNA-binding proteins, and those associated with syndromic/systemic forms of cataract. However, we identified three cataract/lens-associated genes each with one novel homozygous variant including predicted missense substitutions in Prx (p.R167C) and Adamts10 (p.P761L) and a disruptive in-frame deletion variant (predicted missense) in Abhd12 (p.L30_A32delinsS) that were absent in CFW and over 35 other mouse strains. In silico analysis predicted that the missense substitutions in Prx and Adamts10 were borderline neutral/damaging and neutral, respectively, at the protein function level, whereas, that in Abhd12 was functionally damaging. Both the human counterparts of Adamts10 and Abhd12 are clinically associated with syndromic forms of cataract known as Weil-Marchesani syndrome 1 and polyneuropathy, hearing loss, ataxia, retinitis pigmentosa, and cataract syndrome, respectively. Overall, while we cannot exclude Prx and Adamts10, our data suggest that Abhd12 is a promising candidate gene for cataract in the Em/J mouse.
The Effect of Liquidity and Leverage on Profitability in Agricultural Sector Companies

This study aimed to determine the effect of liquidity and leverage on company profitability. The research method used in this research is a descriptive verificative method. The data used is secondary data, namely the type of data obtained indirectly from the first source (company) in the form of a summary of its performance for six years (2013-2018). The sampling technique used purposive sampling, obtained a sample of 14 companies. The data analysis conducted in this study used panel data regression. After the data was obtained, the classical assumption test was carried out (using the linearity, multicollinearity, heteroscedasticity, and autocorrelation test) with Chow's test and Hausman test. This study indicates that the picture of liquidity is in an unfavorable condition, as well as leverage and profitability are in the unfavorable category, and profitability is not significantly affected by liquidity in a negative direction, while leverage has a significant negative effect on profitability.