Miracle-dz/UniFAR-FTUs huggingface dataset:
Cognitive and Non-Cognitive Predictors of the Unifed State Exam Performance of Students from Schools with Regular and Advanced Mathematical Curricula

Background. Exams such as the SAT, ACT, and GCSE are used to give an account of educational outcomes and provide a unified criterion for university admission. The Unified State Exam (USE) aims to fulfill these functions in Russia. All Russian students take two compulsory USE exams, mathematics and Russian, at the end of their school education. Objective. Variability in the mathematics and Russian USE scores is vast, both across and within schools. Our study investigated potential sources of this variability. Design. The sample included 196 students from regular schools (non-selected students) and 306 students from schools with advanced mathematical curriculum (selected students). The mathematical ability (numerical representation, mathematical fluency), intelligence, basic cognitive functions (working memory, reaction time), and mathematical self-efficacy of the students were assessed. We applied structural equation modeling to estimate the proportion of variability in the mathematics and Russian USE scores explained by cognitive predictors and mathematical self-efficacy. Results. In the whole sample, cognitive predictors and mathematical self-efficacy explained 54% of the variation in the mathematics USE scores and 30% of the variation in the Russian USE scores. These effects diminished after the data were analyzed in two groups separately, suggesting that the associations between predictors and exam scores were to a large extent accounted for by group differences (students from regular and specialized schools). Conclusion. The students from the schools with an advanced mathematical curriculum exhibited better cognitive performance, appraised their mathematical abilities higher, and achieved higher mathematics and Russian USE scores, compared to the students from regular schools. Within the groups, cognitive and non-cognitive predictors explained a small part of the variation of the mathematics and Russian USE scores.
An Exploratory Factor Analysis Examining Experiences and Perceptions of Campus Safety for International Students

AN EXPLORATORY FACTOR ANALYSIS EXAMINING EXPERIENCES AND PERCEPTIONS OF CAMPUS SAFETY FOR INTERNATIONAL STUDENTS Sonia H. Ramrakhiani Old Dominion University, 2017 Chair: Christopher Sink Although international students make up a significant percentage of the college population and contribute to higher education institutions in multiple ways, a lack of attention is paid to their safety needs. This dissertation examined the experiences and perceptions of campus safety among international college/university students in the United States. The researcher sampled participants from different institutions around the country, who self-identified as international students. A researcher-developed 53-item Likert scale questionnaire, International Students’ Safety Questionnaire (ISSQ), was administered to the sample. Findings from the exploratory factor analysis (EFA) provided evidence for the four-factor solution for the 26-item ISSQ accounting for 48.65% of the shared variance. Additionally, the ISSQ was found to have adequate internal consistency, a Cronbach alpha of . 85 for the overall instrument and subscale alphas ranging from . 72 to . 81. Salient demographic variables, such as nationality, faith belief, college status and perceived proficiency in English, were found to be significantly linked to derived factor scores. Further, significant positive correlations were found between personality variables, such as extraversion, openness, and neuroticism, and the derived factor scores. Implications for counseling, counselor education, and higher education institutions, along with limitations and directions for future research are included.
Adaptive Scheduling in Real-Time Systems Through Period Adjustment

Real time system technology traditionally developed for safety critical systems, has now been extended to support multimedia systems and virtual reality. A large number of real-time application, related to multimedia and adaptive control system, require more flexibility than classical real-time theory usually permits. This paper proposes an efficient adaptive scheduling framework in real-time systems based on period adjustment. Under this model periodic task can change their execution rates based on their importance value to keep the system underloaded. We propose Period_Adjust algorithm, which consider the tasks whose periods are bounded as well as the tasks whose periods are not bounded.
Home mechanical ventilation in Australia and New Zealand

This study aims to describe the pattern of home mechanical ventilation (HMV) usage in Australia and New Zealand. 34 centres providing HMV in the region were identified and asked to complete a questionnaire regarding centre demographics, patient diagnoses, HMV equipment and settings, staffing levels and methods employed to implement and follow-up therapy. 28 (82%) centres responded, providing data on 2,725 patients. The minimum prevalence of HMV usage was 9.9 patients per 100,000 population in Australia and 12.0 patients per 100,000 population in New Zealand. Variation existed across Australian states (range 4–13 patients per 100,000 population) correlating with population density (r=0.82; p<0.05). The commonest indications for treatment were obesity hypoventilation syndrome (OHS) (31%) and neuromuscular disease (NMD) (30%). OHS was more likely to be treated in New Zealand, in smaller, newer centres, whilst NMD was more likely to be treated in Australia, in larger, older centres. Chronic obstructive pulmonary disease was an uncommon indication (8.0%). No consensus on indications for commencing treatment was found. In conclusion, the prevalence of HMV usage varies across Australia and New Zealand according to centre location, size and experience. These findings can assist HMV service planning locally and highlight trends in usage that may be relevant in other countries.
Social Media and Political Communication

The thrust of this study is to discern how Twitter is changing the patterns of political communication and political expression. In particular, this paper aims to examine the ways Twitter enables the generation of hate speech and intolerance discourses. It is an attempt to obtain a first perception of the hate speech phenomenon within the Greek Twittersphere and to demonstrate what forms of hate speech are being produced, as well as what are the attributes of hate speech messages. Undoubtedly, hate speech on a popular social media platform like Twitter, could possibly obstruct the constructive process of deliberation and also threaten democracy. The research of this paper was based on the case of the murder of a leftist rapper Pavlos Fyssas by a member of Golden Dawn, a right-wing political party in Greece and the tweets that were produced 24 hours after this incident. 2052 tweets related to the event were collected, analyzed and classified into categories based on communication form, content, hate speech characteristics, hate speech targets and hate speech producer’s attributes.
Interferon-Inducible Transmembrane Protein 3 Genetic Variant rs12252 and Influenza Susceptibility and Severity: A Meta-Analysis

Background The pandemic influenza A (H1N1) pdm09 virus, avian influenza A (H5N1) virus, and influenza A (H7N9) virus induced severe morbidity and mortality throughout the world. Previous studies suggested a close association between the interferon-induced transmembrane protein-3 (IFITM3) genetic variant rs12252 and influenza. Here, we explored the correlation between the rs12252 and influenza susceptibility and severity using meta-analysis. Methods Relevant studies published before May 22, 2014 were retrieved from PubMed, ISI web of knowledge, EBSCO, and Cochrane central register of controlled trials databases. Association between rs12252 and influenza susceptibility and severity were determined using statistical analysis of odds ratios (ORs). Results A total of four studies consisting of 445 cases and 4180 controls were included in our analysis. Generally, there is increased risk of influenza in subjects carrying rs12252 in the recessive model (CC vs. CT+TT: OR = 2.35, 95% CI: 1.49-3.70, P<0.001), the dominant model (CC+CT vs. TT: OR=1.60, 95% CI: 1.18–2.22, P=0.003), the homozygote comparison (CC vs. TT: OR=4.11, 95% CI: 2.15–7.84, P<0.001), and the allele contrast (C vs. T: OR=1.67, 95% CI: 1.32–2.13, P<0.001). Stratification analysis of ethnicity and severity revealed a significant increase in influenza susceptibility by IFITM3-SNP rs12252 among both Asian and Caucasian population. SNP rs12252 shows significant impact on severe infections (P<0.05), but not on mild influenza. Besides, our result also associated rs12252 with influenza severity (severe vs. mild: OR=2.37, 95% CI: 1.32–4.25, P=0.004), (severe vs. control: OR=2.70, 95% CI: 1.85–3.94, P<0.001). Conclusion Our meta-analysis suggests a significant association between a minor IFITM3 allele (SNP rs12252-C) with severe influenza susceptibility, but not in mild influenza subjects, in both UK Caucasians and Han Chinese population. The rs12252-C allele causes a 23.7% higher chance of infection and also constitutes a risk factor for more severe influenza.
Involuntary Attention and Distractibility as Evaluated with Event-Related Brain Potentials

This article reviews recent event-related brain potential (ERP) studies of involuntary attention and distractibility in response to novelty and change in the acoustic environment. These studies show that the mismatch negativity, N1 and P3a ERP components elicited by deviant or novel sounds in an unattended sequence of repetitive stimuli index different processes along the course to involuntary attention switch to distracting stimuli. These studies used new auditory-auditory and auditory-visual distraction paradigms, which enable one to assess objectively abnormal distractibility in several clinical patient groups, such as those suffering from closed-head injuries or chronic alcoholism.
Mammals from ‘down under’: a multi-gene species-level phylogeny of marsupial mammals (Mammalia, Metatheria)

Marsupials or metatherians are a group of mammals that are distinct in giving birth to young at early stages of development and in having a prolonged investment in lactation. The group consists of nearly 350 extant species, including kangaroos, koala, possums, and their relatives. Marsupials are an old lineage thought to have diverged from early therian mammals some 160 million years ago in the Jurassic, and have a remarkable evolutionary and biogeographical history, with extant species restricted to the Americas, mostly South America, and to Australasia. Although the group has been the subject of decades of phylogenetic research, the marsupial tree of life remains controversial, with most studies focusing on only a fraction of the species diversity within the infraclass. Here we present the first Methaterian species-level phylogeny to include 80% of the extant marsupial species and five nuclear and five mitochondrial markers obtained from Genbank and a recently published retroposon matrix. Our primary goal is to provide a summary phylogeny that will serve as a tool for comparative research. We evaluate the extent to which the phylogeny recovers current phylogenetic knowledge based on the recovery of “benchmark clades” from prior studies—unambiguously supported key clades and undisputed traditional taxonomic groups. The Bayesian phylogenetic analyses recovered nearly all benchmark clades but failed to find support for the suborder Phalagiformes. The most significant difference with previous published topologies is the support for Australidelphia as a group containing Microbiotheriidae, nested within American marsupials. However, a likelihood ratio test shows that alternative topologies with monophyletic Australidelphia and Ameridelphia are not significantly different than the preferred tree. Although further data are needed to solidify understanding of Methateria phylogeny, the new phylogenetic hypothesis provided here offers a well resolved and detailed tool for comparative analyses, covering the majority of the known species richness of the group.
Chapter 4 : Nonlinear signal modelling and structure selection with applications to genomics

Modeling is a prerequisite for the most fundamental signal processing tasks of signal analysis, detection, classification, denoising, and compression. While linear models are widely used, and they allow elegant theoretical and algorithmic developments, their nonlinear alternatives offer advantages in terms of modeling power and improved performance, which often eclipse the extra cost due to increased complexity. In this chapter we discuss nonlinear signal modeling with two main goals. The first is to present several examples of nonlinear models arising naturally in processing genomic data. The second is to discuss methods for evaluating the complexity of these nonlinear models with information theoretic methods. Several signal processing methods are useful for processing genomic data. Microarray data come originally in the form of microarray images, which need to be preprocessed (denoised and segmented) before the quantities of interest, the gene expression ratios, are estimated from the image. All these stages benefit greatly from nonlinear techniques. The stages involving statistical inference on the gene expression values, can also be formulated as optimal design problems for the structure and the parameters of certain nonlinear predictors. In this chapter we investigate
SOR: A Practical System for Ontology Storage, Reasoning and Search

Ontology, an explicit specification of shared conceptualization, has been increasingly used to define formal data semantics and improve data reusability and interoperability in enterprise information systems. In this paper, we present and demonstrate SOR (Scalable Ontology Repository), a practical system for ontology storage, reasoning, and search. SOR uses Relational DBMS to store ontologies, performs inference over them, and supports SPARQL language for query. Furthermore, a faceted search with relationship navigation is designed and implemented for ontology search. This demonstration shows how to efficiently solve three key problems in practical ontology management in RDBMS, namely storage, reasoning, and search. Moreover, we show how the SOR system is used for semantic master data management.
A Computational Model of Neuro-Glio-Vascular Loop Interactions

We present a computational, biophysical model of neuron-astrocyte-vessel interaction. Unlike other cells, neurons convey “hunger” signals to the vascular network via an intervening layer of glial cells (astrocytes); vessels dilate and release glucose which fuels neuronal firing. Existing computational models focus on only parts of this loop (neuron→astrocyte→vessel→neuron), whereas the proposed model describes the entire loop. Neuronal firing causes release of a neurotransmitter like glutamate which triggers release of vasodilator by astrocytes via a cascade of biochemical events. Vasodilators released from astrocytic endfeet cause blood vessels to dilate and release glucose into the interstitium, part of which is taken up by the astrocyticendfeet. Glucose is converted into lactate in the astrocyte and transported into the neuron. Glucose from the interstitium and lactate (produced from glucose) influx from astrocyte are converted into ATP in the neuron. Neuronal ATP is used to drive the Na+/K+ATPase pumps, which maintain ionic gradients necessary for neuronal firing. When placed in the metabolic loop, the neuron exhibits sustained firing only when the stimulation current is more than a minimum threshold. For various combinations of initial neuronal [ATP] and external current, the neuron exhibits a variety of firing patterns including sustained firing, firing after an initial pause, burst firing etc. Neurovascular interactions under conditions of constricted vessels are also studied. Most models of cerebral circulation describe neurovascular interactions exclusively in the “forward” neuron→vessel direction. The proposed model indicates possibility of “reverse” influence also, with vasomotion rhythms influencing neural firing patterns. Another idea that emerges out of the proposed work is that brain's computations may be more comprehensively understood in terms of neuro-glial-vascular dynamics and not in terms of neural firing alone.
Spatial and spectral features utilization on a Hyperspectral imaging system for rice seed varietal purity inspection

A conventional method to inspect the varietal purity of rice seeds is based on human visual inspection where a random sample is drawn from a batch. This is a tedious, laborious, time consuming and extremely inefficient task. This paper presents an automatic rice seed inspection method using Hyperspectral imaging and machine learning, to automatically detect unwanted seeds from other varieties which may be contained in a batch. Hyperspectral image data from Near-infrared (NER) camera are acquired for six common rice seed varieties. The results of applying two classifiers are presented, a Support Vector Machine (SVM) and a Random Forest (RF), where each consists of six one-versus-rest binary classifiers. The results show that combining spectral and shape-based features derived from the rice seeds, increase precision of the multi-label classification to 84% compared 74% when only visual features are used.
DNA Methylation of Specific CpG Sites in the Promoter Region Regulates the Transcription of the Mouse Oxytocin Receptor

Oxytocin is a peptide hormone, well known for its role in labor and suckling, and most recently for its involvement in mammalian social behavior. All central and peripheral actions of oxytocin are mediated through the oxytocin receptor, which is the product of a single gene. Transcription of the oxytocin receptor is subject to regulation by gonadal steroid hormones, and is profoundly elevated in the uterus and mammary glands during parturition. DNA methylation is a major epigenetic mechanism that regulates gene transcription, and has been linked to reduced expression of the oxytocin receptor in individuals with autism. Here, we hypothesized that transcription of the mouse oxytocin receptor is regulated by DNA methylation of specific sites in its promoter, in a tissue-specific manner. Hypothalamus-derived GT1-7, and mammary-derived 4T1 murine cell lines displayed negative correlations between oxytocin receptor transcription and methylation of the gene promoter, and demethylation caused a significant enhancement of oxytocin receptor transcription in 4T1 cells. Using a reporter gene assay, we showed that methylation of specific sites in the gene promoter, including an estrogen response element, significantly inhibits transcription. Furthermore, methylation of the oxytocin receptor promoter was found to be differentially correlated with oxytocin receptor expression in mammary glands and the uterus of virgin and post-partum mice, suggesting that it plays a distinct role in oxytocin receptor transcription among tissues and under different physiological conditions. Together, these results support the hypothesis that the expression of the mouse oxytocin receptor gene is epigenetically regulated by DNA methylation of its promoter.
Optimal pricing and grant policies for museums

Considering two potential sources of income (public grants and ticket reve-nues), we have defiined a theoretical model where the public agency is the prin-cipal and the manager of the museum is the agent. This model allows us to design the optimal contract between both sides and thus to establish the opti-mal values of grants, ticket prices, budget and effort applied by the manager. Furthermore, we have found a theoretical reason to explain the inelastic pricing strategy that has been found in some of the empirical research on cultural and sports economics. The main conclusion is that the optimal contract allows a Pareto optimum solution in prices that does not change if we introduce moral hazard into this relationship. This solution allows us to conclude that the public agency should regulate ticket prices in accordance with the social valuation. However, public grants and museum budgets would be affected by the existence of this problem, moving the equilibrium away from the Pareto optimum situation. In this case, even with a risk averse manager and a risk neutral public agency, grants and budgets will depend on results because higher budgets related to good results provide the main incentives to increase the managers level of effort. Although the focus of this paper is on museum administration, the model that we have developed can be easily generalized and applied to other institutions, such as schools, sport facilities or NGOs, which are able to raise funds directly from private (e. g. ticket revenues or membership fees) or public sources (e.g. public grants).
Impact of horizontal resolution on climate model forecasts of tropical precipitation and diabatic heating for the TWP‐ICE period

[1] In order to study the impact of horizontal resolution on climate model simulations of tropical moist processes, short-term forecasts using the Community Atmospheric Model (version 4) at several resolutions are performed for a time period encompassing the Tropical Warm Pool-International Cloud Experiment (TWP-ICE). TWP-ICE occurred in the environment of Darwin, Australia in January and February 2006. The experimental period encompasses a number of atmospheric phenomena, such as an MJO passage, mesoscale convective systems, monsoon trough, and active and dry conditions. The CAM is run with four horizontal resolutions: 2°, 1°, 0.5°, and 0.25° latitude-longitude. Simulated profiles of diabatic heating and moistening at the TWP-ICE site show that the model parameterizations respond reasonably well for all resolutions to the sequence of varying conditions imposed by the analyses used to initialize the model. The spatial patterns of global model biases in time mean precipitation are largely unchanged over resolutions, and in some regions the 0.25° model significantly overestimates the observed precipitation. However, there are substantive positive aspects of finer resolution. The diurnally forced circulations over the Maritime continent are more realistically captured by the 0.25° simulation, which is able to better resolve the land-sea breeze. The intensity distribution of rainfall events is also improved at higher resolution through an increased frequency of very intense events and an increased frequency of little or no precipitation. Finally, the ratio of stratiform to convective precipitation systematically increases toward better agreement with observational estimates with increases in resolution.
Distributed information fusion with intermittent observations for large-scale sensor networks

In this paper, we present a robust distributed fusion algorithm to handle intermittent observations via an interacting multiple model (IMM) and a sliding window strategy which is applied to large-scale sensor networks. Intermittent observations are frequently occurred in practice especially when the scale of network becomes larger and sensors are dynamically connected. To solve the problem, we model the communication channel as a jump Markov system and a posterior probability distribution of communication channel characteristics is calculated and incorporated into the filter. By doing so, the distributed Kalman ltering can automatically handle the intermittent observation situations. For the implementation of the distributed fusion, a Kalman-Consensus filter (KCF) is adopted to provide the average consensus based on the estimates of distributed sensors over a large-scale sensor network. In addition, the algorithm is extended to nonlinear systems so as to be implemented for more general dynamic systems. The advantages of proposed algorithm are subsequently verified from target tracking examples for a largescale network with intermittent observations.