Miracle-dz/UniFAR-FTUs huggingface dataset:
THE 2009 OUTBURST OF MAGNETAR 1E 1547–5408: PERSISTENT RADIATIVE AND BURST PROPERTIES

The magnetar 1E 1547–5408 recently exhibited two periods of outburst, beginning on 2008 October 3 and 2009 January 22. Here we present an analysis of the persistent radiative evolution and a statistical study of the burst properties during the 2009 outburst using the Swift X-Ray Telescope. We find that the 1–10 keV flux increased by a factor of ∼500 and hardened significantly, peaking ∼6 hr after the onset of the outburst. The observed pulsed fraction exhibited an anti-correlation with phase-averaged flux. Properties of the several hundred X-ray bursts during the 2009 outburst were determined and compared to those from other magnetar outburst events. We find that the peaks of the bursts occur randomly in phase but that the folded counts that compose the bursts exhibit a pulse which is misaligned with the persistent pulse phase. We also report a correlation between burst hardness and flux. We compare the hardness–flux evolution of the persistent emission of both outbursts to those from other magnetars and find that although an overall trend does exist, the degree of hardening for a given increase in flux is not uniform from source to source. These results are discussed in the context of previous results and within the magnetar model.
Seven Hub Genes Predict the Prognosis of Hepatocellular Carcinoma and the Corresponding Competitive Endogenous RNA Network

Purpose This study was aimed at identifying hub genes and ceRNA regulatory networks linked to prognosis in hepatocellular carcinoma (HCC) and to identify possible therapeutic targets. Methods Differential expression analyses were performed to detect the differentially expressed genes (DEGs) in the four datasets (GSE76427, GSE6764, GSE62232, and TCGA). The intersected DEmRNAs were identified to explore biological significance by enrichment analysis. We built a competitive endogenous RNA (ceRNA) network of lncRNA-miRNA-mRNA. The mRNAs of the ceRNA network were used to perform Cox and Kaplan-Meier analyses to obtain prognosis-related genes, followed by the selection of genes with an area under the curve >0.8 to generate the random survival forest model and obtain feature genes. Furthermore, the feature genes were subjected to least absolute shrinkage and selection operator (LASSO) and univariate Cox analyses were used to identify the hub genes. Finally, the infiltration status of immune cells in the HCC samples was determined. Results A total of 1923 intersected DEmRNAs were identified in four datasets and involved in cell cycle and carbon metabolism. ceRNA network was created using 10 lncRNAs, 67 miRNAs, and 1,923 mRNAs. LASSO regression model was performed to identify seven hub genes, SOCS2, MYOM2, FTCD, ADAMTSL2, TMEM106C, LARS, and KPNA2. Among them, TMEM106C, LARS, and KPNA2 had a poor prognosis. KPNA2 was considered a key gene base on LASSO and Cox analyses and involved in the ceRNA network. T helper 2 cells and T helper cells showed a higher degree of infiltration in HCC. Conclusion The findings revealed seven hub genes implicated in HCC prognosis and immune infiltration. A corresponding ceRNA network may help reveal their potential regulatory mechanism.
Nonlinear control of mechanical systems : a Riemannian geometry approach

Nonlinear control of mechanical systems is a challenging discipline that lies at the intersection between control theory and geometric mechanics. This thesis sheds new light on this interplay while investigating motion control problems for Lagrangian systems. Both stability and motion planning aspects are treated within a unified framework that accounts for a large class of devices such as robotic manipulators, autonomous vehicles and locomotion systems. One distinguishing feature of mechanical systems is the number of control forces. For systems with as many input forces as degrees of freedom, many control problems are tractable. One contribution of this thesis is a set of trajectory tracking controllers designed via the notions of configuration and velocity error. The proposed approach includes as special cases a variety of results on joint and workspace control of manipulators as well as on attitude and position control of vehicles. Whenever fewer input forces are available than degrees of freedom, various control questions arise. The main contribution of this thesis is the design of motion algorithms for vehicles, i.e., rigid bodies moving in Euclidean space. First, an algebraic controllability analysis characterizes the set of reachable configurations and velocities for a system starting at rest. Then, provided a certain controllability condition is satisfied, various motion algorithms are proposed to perform tasks such as short range reconfiguration and hovering. Finally, stabilization techniques for underactuated systems are investigated. The emphasis is on relative equilibria, i.e., steady motions for systems that have a conserved momentum. Local exponential stabilization is achieved via an appropriate splitting of the control authority.
Sparse Code Multiple Access Scheme Based on Variational Learning

Sparse code multiple access (SCMA) technology has been widely studied thanks to its outstanding overload performance, which provides greater device access on limited time-frequency resources. We propose a variational learning based end-to-end SCMA network model termed as V-SCMA. In our model, SCMA codebooks are generated by a deep neural network (DNN) encoder. Motivated by the idea of variational learning, we derive the posterior probability of SCMA codeword send by each user from the point view of binary coding using the variational method. Then, we design an SCMA decoding network to learn this process of approaching the real posterior probability. The SCMA decoding network designed by us is a truncated recurrent neural network, which is simpler than other networks. In addition, a novel loss function is proposed to optimize V-SCMA. Compared with previous works, the new loss function is a tighter variational lower bound which improves the BER performance. Simulation results show that the proposed V-SCMA offers a better bit error rate (BER) performance and a lower computational complexity than conventional schemes.
Early prediction of student knowledge in game-based learning with distributed representations of assessment questions

Game-based learning environments hold significant promise for facilitating learning experiences that are both effective and engaging. To support individual-ised learning and support proactive scaffolding when students are struggling, game-based learning environments should be able to accurately predict student knowledge at early points in students' gameplay. Student knowledge is traditionally assessed prior to and after each student interacts with the learning environment with conventional methods, such as multiple choice content knowledge assessments. While previous student modelling approaches have leveraged machine learning to automatically infer students' knowledge, there is limited work that incorporates the fine-grained content from each question in these types of tests into student models that predict student performance at early junctures in gameplay episodes. This work investigates a predictive student modelling approach that leverages the natural language text of the post-gameplay content knowledge questions and the text of the possible answer choices for early prediction of fine-grained individual student performance in game-based learning environments. With data from a study involving 66 undergraduate students from a large public university interacting with a game-based learning environment for microbiology, Crystal Island , we investigate the accuracy and early prediction capacity of student models that use
Familiar Strategies Feel Fluent: The Role of Study Strategy Familiarity in the Misinterpreted-Effort Model of Self-Regulated Learning

Why do learners not choose ideal study strategies when learning? Past research suggests that learners frequently misinterpret the effort affiliated with efficient strategies as being indicative of poor learning. Expanding on past findings, we explored the integration of study habits into this model. We conducted two experiments where learners experienced two contrasting strategies—blocked and interleaved schedules—to learn to discriminate between images of bird families. After experiencing each strategy, learners rated each according to its perceived effort, learning, and familiarity. Next, learners were asked to choose which strategy they would use in the future. Mediation analyses revealed, for both experiments, that the more mentally effortful interleaving felt, the less learners felt they learned, and the less likely learners were to use it in future learning. Further, in this study, strategy familiarity predicted strategy choice, also mediated by learners’ perceived learning. Additionally, Study 2 verified that, in contrast to learners’ judgments, the less familiar interleaving schedule resulted in better learning. Consequently, learners are making ineffective learning judgments based on their perceptions of effort and familiarity and, therefore, do not make use of optimal study strategies in self-regulated learning decisions.
Improvements in Maximum Likelihood Estimators of Truncated Normal Samples with Prior Knowledge of O: A Simulation Based Study with Application to Historical Height Samples

Researchers analyzing historical data on human stature have long sought an estimator that performs well in truncated-normal samples. This paper reviews that search, focusing on two currently widespread procedures: truncated least squares (TLS) and truncated maximum likelihood (TML). The first suffers from bias. The second suffers in practical application from excessive variability. A simple procedure is developed to convert TLS truncated means into estimates of the underlying population means, assuming the contemporary population standard deviation. This procedure is shown to be equivalent to restricted TML estimation. Simulation methods are used to establish the mean squared error performance characteristics of the restricted and unconstrained TML estimators in relation to several population and sample parameters. The results provide general insight into the bias-precision tradeoff in restricted estimation and a specific practical guide to optimal estimator choice for researchers in anthropometrics.
Comparison of polynucleotide, sodium hyaluronate, and crosslinked sodium hyaluronate for the management of painful knee osteoarthritis: a multi-center, randomized, double-blind, parallel-group study.

OBJECTIVE To compare the clinical effectiveness of sodium polynucleotide, classic hyaluronic acid, and crosslinked hyaluronic acid for the management of painful knee osteoarthritis. DESIGN Randomized, double-blind, parallel-group clinical trial. SETTING Multicenter study. SUBJECTS Patients with chronic painful knee osteoarthritis. METHODS Ninety patients were selected and randomized into polynucleotide, classic hyaluronic acid, and crosslinked hyaluronic acid groups (30 per group). Intra-articular injections of the viscosupplement for each group were administered to the patients three times at one-week intervals. The primary outcome was differences in changes of weight-bearing pain scores at 16 weeks between the groups. The secondary outcomes were changes in the intensity of knee pain during weight-bearing, walking, and rest, and functional disability, quality of life, and adverse events during the 16-week follow-up period. RESULTS At 16 weeks, the polynucleotide group showed a higher reduction in pain score using a Visual Analog Scale score (0-100) than the classic hyaluronic acid (-17.6 [95% CI = -35.1 to -0.1]; P = 0.048) and crosslinked hyaluronic acid (-22.4 [95% CI = -41.5 to -3.3]; P = 0.016) groups. The polynucleotide and crosslinked hyaluronic acid groups showed an early-onset reduction in knee pain during weight-bearing, walking, and rest. All three groups showed reductions in functional disability and improved quality of life at 16 weeks without inter-group differences. No severe adverse events were reported throughout the study period. CONCLUSION Polynucleotide significantly relieves pain more and relieves pain faster in patients with knee osteoarthritis than classic and crosslinked hyaluronic acid, with improved health-related quality of life. CLINICAL TRIAL NUMBER AND REGISTRY URL Clinical Research Information Service (https://cris.nih.go.kr/cris/index.jsp; Identifier: KCT0005308).
The Computation of Economic Equilibria

Apparatus for controlling independent actions of a tower crane, including horizontal trolley travel along the boom track, rotation of the boom itself, and vertical travel of the lifting hook, with a lever, or joy stick, in such manner that the resulting movements are smooth, rapid, and reliably controlled. The lever returns to an origin, or dead center position when not under the influence of an external force.
Exploring Drivers of Staff Engagement in Healthcare Organizations Using Tree-Based Machine Learning Algorithms

Staff engagement in the work environment is vital to organizational success. Engaged staff are motivated and indulged in their work as they have a sense of belonging, commitment, and loyalty toward their employer, which eventually leads to better performance and outcomes. While various organizational factors are related to staff engagement, limited research is available regarding what drives staff engagement and the degree of their importance in healthcare. Leveraging data-driven approaches, in this article, we employ three machine learning algorithms, random forests, gradient boosting, and extra trees, to identify the relative importance of organizational factors affecting staff engagement. We use hospital-level aggregate survey data from hospitals in the U.K. While staff engagement is the outcome variable, the following factors are used as organizational factors in the prediction model and feature importance analysis: equality, diversity, and inclusion, safety culture, health and wellbeing, immediate managers, quality of appraisals, quality of care, bullying and harassment, violence, and team working. All the algorithms provide comparable prediction results with similar feature importance ranking with respect to prediction accuracy. The results suggest that safety culture is the most influential factor related to staff engagement, followed by the team working. Healthcare managers and decision makers can benefit from this data-driven application to make informed decisions in resource allocation and prioritization efforts to improve staff engagement.
Slope Entropy Characterisation: The Role of the δ Parameter

Many time series entropy calculation methods have been proposed in the last few years. They are mainly used as numerical features for signal classification in any scientific field where data series are involved. We recently proposed a new method, Slope Entropy (SlpEn), based on the relative frequency of differences between consecutive samples of a time series, thresholded using two input parameters, γ and δ. In principle, δ was proposed to account for differences in the vicinity of the 0 region (namely, ties) and, therefore, was usually set at small values such as 0.001. However, there is no study that really quantifies the role of this parameter using this default or other configurations, despite the good SlpEn results so far. The present paper addresses this issue, removing δ from the SlpEn calculation to assess its real influence on classification performance, or optimising its value by means of a grid search in order to find out if other values beyond the 0.001 value provide significant time series classification accuracy gains. Although the inclusion of this parameter does improve classification accuracy according to experimental results, gains of 5% at most probably do not support the additional effort required. Therefore, SlpEn simplification could be seen as a real alternative.
Online Supportive Conversations and Reflection Sessions (OSCaRS): A Feasibility Pilot with Care Home Staff during the Pandemic

Care homes care for people with complex needs, supporting them to the end of life and are now being seen as the ‘de facto’ hospice. Reflective debriefing for care home staff has been found to help support staff and provide an educative and communicative function when a resident dies. Pre-COVID-19, one of the authors had been conducting reflective debriefings face-to-face with care home staff but when COVID-19 struck, face-to-face sessions were impossible. An online format was developed with the aim of providing emotional support and practice-based learning in relation to death and dying through reflection. This study assessed the acceptability and feasibility of delivering online supportive conversations and reflective sessions (OSCaRS) on palliative and end of life care to care home staff during the pandemic. A mixed methods study design was undertaken in April to September 2020. Qualitative data comprised of digital recordings of sessions and semi-structured interviews with OSCaRS participants, managers and session facilitators. An online survey was sent to all staff and had a response rate of 12%. Eleven OSCaRS were conducted over ten weeks. Thirty-four staff members attended one or more sessions. Three overarching themes were identified from the data: pressures of working in a pandemic, practicalities of delivering online support and, practice development opportunities. Engaging care home staff in online structured supportive conversations and reflections in relation to death and dying is acceptable, feasible and valuable for providing support with the pressures of working in a pandemic. There is value for OSCaRS to continue as online sessions as they provide care home staff access to practice-based learning and support from professionals and allows specialists based in a range of settings to in-reach into care homes in an efficient way. Future implementation must consider the availability of sufficient devices with cameras to aid participation, timing and frequency of sessions to accommodate staff workflows, the engagement and support of managers and post-session support.
Neural Network for Optical Performance Estimation and Advanced Lens Combination

We developed an algorithm to estimate the performance of an optical system based on the errors of its individual components. After a short training period with classical simulated systems, the performance evaluation for tolerancing could be accelerated by a factor of about three million. Additionally, we propose a probability-based sorting algorithm to combine individual, erroneous components in order to compensate for the tolerance budget within the system and increase the overall yield.
Investigation on the optimal machining of Mg-based composites considering surface roughness, tool life, cutting forces, and productivity

Machining of metal matrix composites (MMC) are being increasingly taken into consideration due to MMCs’ especial features like high strength to weight ratio, resistance to wear and creep. In this paper, effects of cutting parameters on the surface quality, tool life, and cutting force when machining of AZ91/SiC composites are analyzed to make the process optimized. To this end, a design of experiment based on the response surface methodology are done to consider variations of cutting parameters such as cutting speed, feed rate, and depth of cut. Challenges like particles pulled out and tool worn out in the machining process are evaluated. Subsequently, the results of cutting responses are analyzed and a multi-objective optimization is applied on the data to obtain the best surface quality, the highest production volume and the condition at which tool life is longest. The results showed although increasing the feed rate from 0.05 to 0.1 mm/rev leads to the tool life reduction about 70%, it enhances the productivity considerably about 100% for composites with volume fraction 5%. Moreover, at cutting speed 41.05 m/min and feed rate 0.07 mm/rev, machining process of AZ91/SiC-vol.5% becomes efficient.
Modulated wave trains in generalized Kuramoto-Sivashinksi equations

This paper is concerned with the stability of periodic wave trains in a generalized Kuramoto-Sivashinski (gKS) equation. This equation is useful to describe the weak instability of low frequency perturbations for thin film flows down an inclined ramp. We provide a set of equations, namely Whitham's modulation equations, that determines the behaviour of low frequency perturbations of periodic wave trains. As a byproduct, we relate the spectral stability in the small wavenumber regime to properties of the modulation equations. This stability is always critical since 0 is a 0-Floquet number eigenvalue associated to translational invariance.
Enhanced Electrochemical Performances of Mn3O4/Heteroatom-Doped Reduced Graphene Oxide Aerogels as an Anode for Sodium-Ion Batteries

Owing to their high theoretical capacity, transition-metal oxides have received a considerable amount of attention as potential anode materials in sodium-ion (Na-ion) batteries. Among them, Mn3O4 has gained interest due to the low cost of raw materials and the environmental compatibility. However, during the insertion/de-insertion process, Mn3O4 suffers from particle aggregation, poor conductivity, and low-rate capability, which, in turn, limits its practical application. To overcome these obstacles, we have successfully prepared Mn3O4 nanoparticles distributed on the nitrogen (N)-doped and nitrogen, sulphur (N,S)-doped reduced graphene oxide (rGO) aerogels, respectively. The highly crystalline Mn3O4 nanoparticles, with an average size of 15–20 nm, are homogeneously dispersed on both sides of the N-rGO and N,S-rGO aerogels. The results indicate that the N-rGO and N,S-rGO aerogels could provide an efficient ion transport channel for electrolyte ion stability in the Mn3O4 electrode. The Mn3O4/N- and Mn3O4/N,S-doped rGO aerogels exhibit outstanding electrochemical performances, with a reversible specific capacity of 374 and 281 mAh g−1, respectively, after 100 cycles, with Coulombic efficiency of almost 99%. The interconnected structure of heteroatom-doped rGO with Mn3O4 nanoparticles is believed to facilitate fast ion diffusion and electron transfer by lowering the energy barrier, which favours the complete utilisation of the active material and improvement of the structure’s stability.
Assessing the Factors Related to a Start-Up’s Valuation Using Prediction and Causal Discovery

Research indicates that investors rely on various criteria to evaluate early-stage companies. However, past research in this area has focused on subsets of factors and does not distinguish between the predictors and causal determinants of start-up valuation. In our study, we applied machine learning and causal discovery to analyze a comprehensive dataset with 57 independent variables and 2,366 valuations of start-ups in the United Kingdom. The results show a strong relationship between good predictors and causal determinants of valuation. However, noncausal variables may still be useful for prediction, and inversely, some observed causes may not help in the prediction task.
Techniques for Developing Large Scale Fuzzy Logic Systems

In this paper, we describe various techniques used to make Intuitionistic Fuzzy Logic Systems amenable to operating on applications with large numbers of inputs. A rule reduction technique known as Combs method is combined with an automated tuning process based on Particle Swarm Optimization. A second stage of tuning on rule weights results in improved performance and further reduction in the size of the rule-base. The entire process has been developed to operate within the Matlab software environment. The technique is tested against the Wisconsin Breast Cancer Database. The use of these tools shows great promise in significantly expanding the range and complexity of problems that can be addressed using Atanassov’s Intuitionistic Fuzzy Logic.