Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of educational facilitation approaches for Grade R English Additional Language learning in rural Mpumalanga

The Early Childhood Development Manager in Mpumalanga is faced with the problem of providing evidence-based guidance of the best facilitation approach in the Grade R context. An investigation on the effect of facilitation, i.e. play-based or formal instruction, on Grade R performance scores in English Additional Language (EAL) learning was conducted. Literature findings attest to formal learning contributing to better performance scores than play-based learning, yet most rural schools in Mpumalanga use the play-based approach. The English Language Proficiency (ELP) standards assessment tool is reported to have no cultural bias and was used to collect the data. The tool assessed learners’ listening and speaking skills in EAL. A quantitative methodology was followed, using a static two-group comparison design. Participants in the two groups were matched according to age and all had a similar exposure period to EAL learning, a rural upbringing, poverty level, and all were mainstream learners. Inter-rater reliability was obtained since two raters assessed learners’ proficiency in EAL skills. A one-way Analysis of Variance (ANOVA) was used to analyse the data. It was found that the formal based approach contributed to better EAL scores when compared to the play-based approach. Implications for practice are discussed. Keywords : EAL learning; ELP standards assessment tool; formal instruction;Grade R facilitation; learners’ first language; play-based approach
Putting Semantics into Semantic Roles

While there have been many proposals for theories of semantic roles over the years, these models are mostly justified by intuition and the only evaluation methods have been inter-annotator agreement. We explore three different ideas for providing more rigorous theories of semantic roles. These ideas give rise to more objective criteria for designing role sets, and lend themselves to some experimental evaluation. We illustrate the discussion by examining the semantic roles in TRIPS.
The Long Noncoding RNA Cancer Susceptibility 9 and RNA Binding Protein Heterogeneous Nuclear Ribonucleoprotein L Form a Complex and Coregulate Genes Linked to AKT Signaling

The identification of viability‐associated long noncoding RNAs (lncRNAs) might be a promising rationale for new therapeutic approaches in liver cancer. Here, we applied an RNA interference screening approach in hepatocellular carcinoma (HCC) cell lines to find viability‐associated lncRNAs. Among the multiple identified lncRNAs with a significant impact on HCC cell viability, we selected cancer susceptibility 9 (CASC9) due to the strength of its phenotype, expression, and up‐regulation in HCC versus normal liver. CASC9 regulated viability across multiple HCC cell lines as shown by clustered regularly interspaced short palindromic repeats interference and single small interfering RNA (siRNA)–mediated and siRNA pool–mediated depletion of CASC9. Further, CASC9 depletion caused an increase in apoptosis and a decrease of proliferation. We identified the RNA binding protein heterogeneous nuclear ribonucleoprotein L (HNRNPL) as a CASC9 interacting protein by RNA affinity purification and validated it by native RNA immunoprecipitation. Knockdown of HNRNPL mimicked the loss‐of‐viability phenotype observed upon CASC9 depletion. Analysis of the proteome (stable isotope labeling with amino acids in cell culture) of CASC9‐depleted and HNRNPL‐depleted cells revealed a set of coregulated genes which implied a role of the CASC9:HNRNPL complex in AKT signaling and DNA damage sensing. CASC9 expression levels were elevated in patient‐derived tumor samples compared to normal control tissue and had a significant association with overall survival of HCC patients. In a xenograft chicken chorioallantoic membrane model, we measured decreased tumor size after knockdown of CASC9. Conclusion: Taken together, we provide a comprehensive list of viability‐associated lncRNAs in HCC; we identified the CASC9:HNRNPL complex as a clinically relevant viability‐associated lncRNA/protein complex which affects AKT signaling and DNA damage sensing in HCC.
Missed Opportunities: Optimal Investment Timing When Information is Costly

Abstract Real option analysis typically assumes that projects are continuously evaluated and launched at precisely the time determined to be optimal, but real world projects cannot be managed in this way because of the costs of formally evaluating an investment opportunity. This paper shows that immediate investment is more attractive if evaluation costs are high or the amount of information to be revealed by an evaluation is large. The optimal delay until a reevaluation is long if evaluation costs are high or the amount of information to be revealed by an evaluation is small. The reduction in the value of project rights is especially severe when the value of the completed project is strongly mean reverting because then precision in investment timing is particularly important.
Frequency Dependent Model of Leakage Inductance for Magnetic Components

Theoretical methods for the calculation of winding losses with respect to frequency have been well documented, but the variation in leakage inductance of magnetic components due to frequency has been less well addressed. In this paper Dowell’s well-known theoretical approach is tested using measurements and finite element analyses. The results presented show deficiencies in the accuracy of models generated using Dowell’s approach and highlight the tradeoffs between model complexity, simulation time and accuracy when the finite element analysis method is applied. An alternative behavioral model is presented which models the frequency variation in leakage inductance accurately, is easy to characterize from simple measurements or calculations and is robust.
Sterile Neutrinos for Warm Dark Matter and the Reactor Anomaly in Flavor Symmetry Models

We construct a flavor symmetry model based on the tetrahedral group A{sub 4} in which the right-handed neutrinos from the seesaw mechanism can be both keV warm dark matter particles and eV-scale sterile neutrinos. This is achieved by giving the right-handed neutrinos appropriate charges under the same Froggatt-Nielsen symmetry responsible for the hierarchy of the charged lepton masses. We discuss the effect of next-to-leading order corrections to deviate the zeroth order tri-bimaximal mixing. Those corrections have two sources: (i) higher order seesaw terms, which are important when the seesaw particles are eV-scale, and (ii) higher-dimensional effective operators suppressed by additional powers of the cut-off scale of the theory. Whereas the mixing angles of the active neutrinos typically receive corrections of the same order, the mixing of the sterile neutrinos with the active ones is rather stable as it is connected with a hierarchy of mass scales. We also modify an effective A{sub 4} model to incorporate keV-scale sterile neutrinos.
Extraction and Recognition of Planes in a Single Image Using Matlab

We present a novel method to recognize planar structures in a single image and estimate their 3D orientation. This is done by exploiting the relationship between image appearance and 3D structure, using machine learning methods with supervised training data. As such, the method does not require specific features or use geometric cues, such as vanishing points. We employ general feature representations based on spatiograms of gradients and color, coupled with relevance vector machines for classification and regression. We first show that using hand-labeled training data, we are able to classify pre-segmented regions as being planar or not, and estimate their 3D orientation. We then incorporate the method into a segmentation algorithm to detect multiple planar structures from a previously unseen image. INTRODUCTION: This project is concerned with the automatic extraction of 3D structure from single images. While the creation of 3D models of real-world scenes from image data has been a topic of interest for a long time, it is usual for this to involve either multiple views of a scene or video data, exploiting parallax to obtain information about scene depth. Inferring depth from only a single image is much more challenging. However, previous works have shown that a number of image cues can be exploited to extract information about depth, shape, or other 3D structure.Two prominent existing methods are those of Saxena et al. [32] and Hoiem et al. [19]. The former is able to recover an approximate depth map for an image having learned the relationship between image appearance and ground truth depth maps derived from laser scanning. RELATED WORK: Here we discuss examples of prior work on extracting planar structure from single images. This can be divided into two main categories: methods which explicitly use geometric properties, such as parallel lines and texture; and work which aims to recognize structure based on learning from training examples. Given two or more sets of parallel lines lying on a plane, their respective vanishing points uniquely define the plane‘s 3D orientation [17]. Thus, detecting such line features in an image enables the extraction of structure, providing they lie on a common plane. One approach is to detect rectangular structures, such as windows or doors, which provide orthogonal parallel lines in the same plane [22], [27], which can be used for basic camera pose recovery and wide baseline matching. However, these methods rely on orthogonal, Manhattan-like structure, and reliable line detection, hence limiting applicability. Machine Learning Methods: More recent work has looked at techniques for learning the relationship between appearance and structure. A good example of such a technique is by Torralba and Oliva [35], who estimate overall depth using knowledge that certain types of structure tend to appear at particular distances. However, this work
A proposal to a decision support system with learning analytics

Learning Analytics has acquire significance on the educational field due to it discovers hidden patterns once the data is processed and integrated in real case of use. Additionally it offers to stakeholder's support to improve their task into educational context. Authors integrated Learning Analytics techniques with a Decision Support System in a proposal to help Educational Institution's administrators in developing decision making process and collaterally improve student performance. In this paper, authors define a Decision Support System and describe its usage in a Educational Institution. The functionalities of Learning Analytics are presented and the integration to help departments administrators to improve decision impact.
Ready or not?

All clinical teachers will be familiar with the challenge of deciding how much independence to allow a learner under their supervision. Too little and you stifle their educational growth, while wasting your time; too much and you might endanger patient safety, while overwhelming the learner. Yet trends towards competency-based training and changes to the traditional master–apprentice relationship require us to make reliable and justifiable judgments of when our trainees have become competent.
Design Thinking using the Blockchain: Enable Traceability of Intellectual Property in Problem-Solving Processes for Open Innovation

In this vision paper, we present an approach that makes it possible to protect developed ideas and early concepts even during their systematical development. We take the Design Thinking process as an example, in which interfaces are used for individual stages (understand, observe, define, ideate, prototype, test) to digitally record verbal, written or sketched, and even modeled or constructed outcome. This outcome is recorded and linked to the originating person. To guarantee both proof-of-existence and proof-of-origin, a unique hash is generated from each digital artifact stored and embedded into the Bitcoin Blockchain by the OriginStamp decentralized trusted timestamping service. Once this unique fingerprint is embedded in a transaction in the underlying Blockchain network, it can be proven where particular contributions originated due to the characteristics of Blockchain architecture. By setting up a decentralized tamper-proof means of record keeping, the entire innovation chain from the first ideation to the beginning of production is verifiably stored. By providing a clear proof-of-origin, all innovators (even competitors) could continue to work on existing problem-solving process and add their contribution proportionately, depending on the state of innovation development. This concept enables an Open Innovation ecosystem, which has the potential to increase the innovation potential of companies immensely. Additionally, inventions that are not patentable because they do not comply with the strict regulations of patent law can still be published and protected because the information about the origin of the respective contribution is guaranteed.
H.264/AVC error resilient video streaming using leaky prediction

In this paper, we investigate the efficacy of the weighted prediction feature provided within H.264/AVC video coding standard for error resilient video streaming. Leaky prediction has been proposed for scalable and non-scalable video coding to effectively combat transport errors. However, all prior results are based on non standard coding methods and no results have been available on the effectiveness of leaky prediction that is supported by a video coding standard. The weighted prediction feature in H.264/AVC was originally designed for improving the coding efficiency especially in presence of fading in video sequences. This paper presents a performance analysis of H.264/AVC weighted prediction feature that balances the trade-off between coding efficiency and error resilience. A theoretical analysis of rate-distortion performance of leaky prediction is provided and closed-form rate-distortion functions are derived for the error free and error drift scenarios. The theoretical results conform well to the operational results with respect to different choices of the leaky factor.
Genetic Characterization of Animal Brucella Isolates from Northwest Region in China

Animal brucellosis is a reemerging disease in China, particular in northwest China. The Brucella species (even genus) are highly conserved; therefore the use of Multilocus sequencing typing (MLST: based on conserved housekeeping loci) is more suitable for discrimination at species or biovar level on Brucella. In this study, MLST was used to analyze the characterization of Brucella from sheep and yaks during 2015 and 2016. All 66 isolates were collected from northwest China, including Inner Mongolia, Xinjiang, Qinghai, and Gansu provinces. Isolates were cultured on Brucella agar medium and identified by MLST. MLST identified five ST types: ST8 (n = 55), ST7 (n = 2), ST3 (n = 5), ST1 (n = 2), and ST14 (n = 2). This analysis revealed that B. melitensis isolates exhibited high single genotypes (ST8) in the most northwest China. MLST of isolates provides helpful information on understanding genetic characterization of Brucella in northwest China.
An improved control for DC-link fluctuation during voltage dip based on DFIG

The main purpose of this work is to limit the DC-link voltage fluctuation in doubly fed induction generator (DFIG) based wind turbine system to enhance low voltage ride through capability. Grid-side and rotor-side converters control has been addressed to avoid disconnection of turbine during fault conditions but these works, thoughts, often ignore dc-bus voltage ripples limitation during voltage dips, and fault ride through of DFIG may not be satisfactory without minimizing the DC-link voltage fluctuations within their acceptable ranges. In this work an improved control strategy to limit the DC-link voltage fluctuation under normal and fault conditions is implemented. A modified control scheme has been applied to the direct current loop of the grid-side controller. If the grid filter active current setting during the voltage dip is determined, the sum of instantaneous powers of the grid and the grid filter is nearly equal to the instantaneous output rotor power, the DC-link voltage will not change significantly and will remain nearly constant. Simulations performed by Matlab software demonstrate the performances of the proposed method in limiting DC-bus voltage fluctuation during severe voltage dip.
CONVOLUTIONAL NEURAL NETWORK FOR SKIN LESION SEGMENTATION

Automatic segmentation of skin lesions in dermoscopy and clinical images is a common initial step in computer aided diagnosis. However, the low contrast of lesion boundaries and the existence of misleading image artifacts (e.g., hair), make segmenting skin lesions a challenging task. We propose a deep auto-context architecture that incorporates image appearance information as well as contextual information to predict the pixel-wise probability of a skin lesion. A sequence of fully convolutional networks is trained in a consecutive manner, where the input of each classifier is the original image concatenated with a degraded a posteriori probability estimated by the previous classifier. In contrast to common approaches that use morphological operations or thresholds to correct irregularities in the predicted lesion segmentation mask, our auto-context architecture efficiently refines the skin segmentation without any post-processing. Our experiments demonstrate that using our deep auto-context framework improves the segmentation performance of U-Net by 3.5% in terms of Dice similarity coefficient.
Clustering under composite generative models

This paper studies clustering of data samples generated from composite distributions using the Kolmogorov-Smirnov (KS) based K-means algorithm. All data sequences are assumed to be generated from unknown continuous distributions. The maximum intra-cluster KS distance of each distribution cluster is assumed to be smaller than the minimum inter-cluster KS distance of different clusters. The analysis of convergence and upper bounds on the error probability are provided for both cases with known and unknown number of clusters. Furthermore, it is shown that the probability of error decays exponentially as the number of samples in each data sequence goes to infinity, and the error exponent is only a function of the difference of the inter-cluster and intra-cluster KS distances. The analysis is validated by simulation results.
Psychological resources, satisfaction, and career identity in the work transition: an outlook on Sicilian college students

Background The phases of career building today bring out a more complex process than in previous decades. Starting from the literature review, the university-to-work transition is considered a very important step in the future career of the graduates, and it involves some psychological resources and requires specific abilities. Methods Research has examined the psychological resources that students at the end of a degree course can use in the university-to-work transition. The aim of the study is to verify the relationship between academic satisfaction and career identity, and the mediational role of readiness and confidence on this relationship. A group of 438 students were assigned to complete a questionnaire in order to examine the relationship between academic satisfaction and career identity and the role of core components of psychological resources: readiness and confidence as mediator. Results The results indicated both a direct relationship between academic satisfaction and career identity and a mediated relationship with the influence of readiness and confidence for a transition. Adding to our results, we assert that academic satisfaction has a directed effect on confidence during the transition and is a predictor of career identity, both directly and by the mediation of readiness in career transitions. Conclusion Career identity has implication for exploratory behavior, thus increasing the motivation and mindfulness that create a virtuous circle, influencing the development of knowledge and skills, which are the base of proactivity and confidence in construction of one’s future career.
Low-Level Laser Therapy for Temporomandibular Disorders: A Systematic Review with Meta-Analysis

Objectives We systematically reviewed randomized controlled trials (RCTs) of the effect of low-level laser therapy (LLLT) versus placebo in patients with temporomandibular disorder (TMD). Methods A systematic search of multiple online sources electronic databases was undertaken. The methodological quality of each included study was assessed using the modified Jadad scale, and the quality of evidence was evaluated using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) system. Results A total of 31 RCTs were included. Total modified Jadad scale scores showed that the methodological quality was high in 30 studies and low in 1 study. Combining data from all clinically heterogeneous studies revealed positive effects of LLLT on pain relief, regardless of the visual analogue scale (VAS) score or the change of VAS score between the baseline and the final follow-up time point, while dosage analyses showed discrepant results about the effects of high or low doses for patients with TMD. Follow-up analyses showed that LLLT significantly reduced pain at the short-term follow-up. Temporomandibular joint function outcomes indicated that the overall effect favored LLLT over placebo. Conclusion This systematic review suggests that LLLT effectively relieves pain and improves functional outcomes in patients with TMD.
Biotransformation of Geniposide into Genipin by Immobilized Trichoderma reesei and Conformational Study of Genipin

Trichoderma reesei QM9414, Trichoderma viride 3.316, Aspergillus niger M85, and Aspergillus niger M92 were screened for hydrolyzing geniposide into genipin. T. reesei was selected according to the β-glucosidase activity of the fermentation broths using geniposide as a substrate. T. reesei was immobilized by embedding method using sodium alginate as the carrier. Geniposide was hydrolyzed by immobilized T. reesei at 28°C (200 rpm) for 34 h, and the yield of genipin was 89%. The product was purified and identified by UV, IR, EIMS, and 1H-NMR. Since there were two sets of signals in 1H-NMR spectra, a series of experiments were performed and verified that the existence of two conformations was the main reason. Generally, biotransformation of geniposide into genipin by immobilized T. reesei provides a promising solution to the genipin production.