Miracle-dz/UniFAR-FTUs huggingface dataset:
Spanish Cross-Cultural Adaptation of the Australian Pelvic Floor Questionnaire

The main objectives of this study were to carry out the translation and cross-cultural adaptation of the Australian Pelvic Floor Questionnaire (APFQ) into Spanish and the evaluation of its psychometric properties of validity and reliability in the Spanish population. The APFQ was translated into Spanish and back-translated into its original language by native speakers; it was verified that there was a semantic similarity. A pilot test was carried out on a group of 10 women. The study sample was made up of 104 subjects. They were asked to fill in the APFQ twice, 15 days apart. Codes were assigned so they could link to the test and retest. The Questionnaire on Pelvic Floor Dysfunctions–short version (PFDI-20) and the Women’s Sexual Function Questionnaire (FSM) were also completed. The reliability, criterion and construct validity, and stability were studied. A Cronbach’s alpha of 0.795 was obtained from the complete questionnaire. For each dimension, Cronbach’s alpha was 0.864 for bladder function; 0.796 for bowel function; 0.851 for prolapse; and 0.418 for sexual function (0.67 with the suppression of item 37). The APFQ shows a significant correlation with PFDI-20 in urinary function (rho: 0.704, p = 0.000), intestinal function (rho: 0.462, p = 0.000), and prolapse symptoms (rho: 0.337, p = 0.000). The test-retest analysis showed high reproducibility. The Spanish version of the APFQ is a reliable and valid tool to assess symptoms and impacts on quality of life due to pelvic floor dysfunctions in the Spanish population. However, a review of some of its items could increase its reliability.
ROC CURVE ANALYSIS OF DIFFERENT HYBRID FEATURE DESCRIPTORS USING MULTI CLASSIFIERS

Tremendous success of machine learning algorithms at pattern recognition creates interest in new inventions. Machine learning in an era of big data is that significant hierarchical relationships within the data can be discovered algorithmically than other handcraft like features. In this study, Convolutional Neural Network (CNN) is used as feature descriptors in pulmonary malignancy prediction. Various feature descriptors such as Histogram of Oriented Gradient (HOG), Extended Histogram of Oriented Gradient (EXHOG) and Linear Binary Pattern (LBP) descriptors are analyzed with classifiers such as Random Forest (RF), Decision Tree (DT), K-Nearest Neighbour (KNN) and Support Vector Machine (SVM) for Computed Tomography (CT) The phenotype features of pulmonary nodules are important cues for identification. The nodule solidity is an important cue for white blob area identification. The method is analyzed in Lung Image Database Consortium (LIDC) dataset. Receivers Operating Characteristics (ROC) curves show the graphical summaries of detectors performance. It is proved that CNN based feature extraction with SVM classifier works well in pulmonary malignancy prediction.
Recovery 3D Shape from a 2D Image Sequence

0.1 Projective Factorization . 10.2 Rotation Constraints . 30.3 Triad Rotation Constraints . 30.4 Triad Basis Constraints . 50.5 Solutions to Rigid Body 3D Recovery (RSFM) . 70.6 Solutions to Non-Rigid Body 3D Recovery (NRSFM) . 81
In Vitro-Produced Equine Blastocysts Exhibit Greater Dispersal and Intermingling of Inner Cell Mass Cells than In Vivo Embryos

In vitro production (IVP) of equine embryos is increasingly popular in clinical practice but suffers from higher incidences of early embryonic loss and monozygotic twin development than transfer of in vivo derived (IVD) embryos. Early embryo development is classically characterized by two cell fate decisions: (1) first, trophectoderm (TE) cells differentiate from inner cell mass (ICM); (2) second, the ICM segregates into epiblast (EPI) and primitive endoderm (PE). This study examined the influence of embryo type (IVD versus IVP), developmental stage or speed, and culture environment (in vitro versus in vivo) on the expression of the cell lineage markers, CDX-2 (TE), SOX-2 (EPI) and GATA-6 (PE). The numbers and distribution of cells expressing the three lineage markers were evaluated in day 7 IVD early blastocysts (n = 3) and blastocysts (n = 3), and in IVP embryos first identified as blastocysts after 7 (fast development, n = 5) or 9 (slow development, n = 9) days. Furthermore, day 7 IVP blastocysts were examined after additional culture for 2 days either in vitro (n = 5) or in vivo (after transfer into recipient mares, n = 3). In IVD early blastocysts, SOX-2 positive cells were encircled by GATA-6 positive cells in the ICM, with SOX-2 co-expression in some presumed PE cells. In IVD blastocysts, SOX-2 expression was exclusive to the compacted presumptive EPI, while GATA-6 and CDX-2 expression were consistent with PE and TE specification, respectively. In IVP blastocysts, SOX-2 and GATA-6 positive cells were intermingled and relatively dispersed, and co-expression of SOX-2 or GATA-6 was evident in some CDX-2 positive TE cells. IVP blastocysts had lower TE and total cell numbers than IVD blastocysts and displayed larger mean inter-EPI cell distances; these features were more pronounced in slower-developing IVP blastocysts. Transferring IVP blastocysts into recipient mares led to the compaction of SOX-2 positive cells into a presumptive EPI, whereas extended in vitro culture did not. In conclusion, IVP equine embryos have a poorly compacted ICM with intermingled EPI and PE cells; features accentuated in slowly developing embryos but remedied by transfer to a recipient mare.
Which supervised machine learning algorithm can best predict achievement of minimum clinically important difference in neck pain after surgery in patients with cervical myelopathy? A QOD study.

OBJECTIVE The purpose of this study was to evaluate the performance of different supervised machine learning algorithms to predict achievement of minimum clinically important difference (MCID) in neck pain after surgery in patients with cervical spondylotic myelopathy (CSM). METHODS This was a retrospective analysis of the prospective Quality Outcomes Database CSM cohort. The data set was divided into an 80% training and a 20% test set. Various supervised learning algorithms (including logistic regression, support vector machine, decision tree, random forest, extra trees, gaussian naïve Bayes, k-nearest neighbors, multilayer perceptron, and extreme gradient boosted trees) were evaluated on their performance to predict achievement of MCID in neck pain at 3 and 24 months after surgery, given a set of predicting baseline features. Model performance was assessed with accuracy, F1 score, area under the receiver operating characteristic curve, precision, recall/sensitivity, and specificity. RESULTS In total, 535 patients (46.9%) achieved MCID for neck pain at 3 months and 569 patients (49.9%) achieved it at 24 months. In each follow-up cohort, 501 patients (93.6%) were satisfied at 3 months after surgery and 569 patients (100%) were satisfied at 24 months after surgery. Of the supervised machine learning algorithms tested, logistic regression demonstrated the best accuracy (3 months: 0.76 ± 0.031, 24 months: 0.773 ± 0.044), followed by F1 score (3 months: 0.759 ± 0.019, 24 months: 0.777 ± 0.039) and area under the receiver operating characteristic curve (3 months: 0.762 ± 0.027, 24 months: 0.773 ± 0.043) at predicting achievement of MCID for neck pain at both follow-up time points, with fair performance. The best precision was also demonstrated by logistic regression at 3 (0.724 ± 0.058) and 24 (0.780 ± 0.097) months. The best recall/sensitivity was demonstrated by multilayer perceptron at 3 months (0.841 ± 0.094) and by extra trees at 24 months (0.817 ± 0.115). Highest specificity was shown by support vector machine at 3 months (0.952 ± 0.013) and by logistic regression at 24 months (0.747 ± 0.18). CONCLUSIONS Appropriate selection of models for studies should be based on the strengths of each model and the aims of the studies. For maximally predicting true achievement of MCID in neck pain, of all the predictions in this balanced data set the appropriate metric for the authors' study was precision. For both short- and long-term follow-ups, logistic regression demonstrated the highest precision of all models tested. Logistic regression performed consistently the best of all models tested and remains a powerful model for clinical classification tasks.
Spectral activity of the Seyfert galaxy Markarian 6 in 1970–1991

We present results of the reduction of the archive spectra of the nucleus of Mrk 6 obtained in 1970-1991 using the image tube spectrograph at the 2.6-m Shajn telescope of the Crimean Astrophysical Observatory. We analyze 363 spectra for 181 dates obtained in the H spectral region. The spectra in the H region (194 spectra for 179 dates) were used to get light curves. The light curves in the H and H lines and also in the adjacent continuum show appreciable variability. Broad-line profile parameters (such as centroid, width, blue-to-red ratio of fluxes) seem to be independent of or dependent very weakly on the continuum flux. However, there is a tendency for the blue-to-red ratio of fluxes to increase with increasing continuum flux, and also a weak tendency for the profile width (profile dispersion) to decrease with increasing continuum flux. An anti- correlation between H shapes at the blue and red wings, positive correlation between blue shape and line/continuum flux, and anti-correlation between red shape and line/continuum flux show that the blue segments respond slightly better to the continuum changes than the red segment and total H line. We assume that the observed broad profile consists of at least two components with fixed profiles. We found that one of the components is nearly symmetric and has a single peak, while the other component has a blue bump and extended red wing. In terms of such a two-broad-components profile model, the evolution of the observed H profiles can be roughly reproduced by changes in the relative strength of the two variable components. However, the evolution of the broad H profile in Mrk 6 is complicated and not clear at all. The evolution is driven, most likely, by a set of various factor, including a multi-component BLR model with redistribution of ionizing radiation between dierent parts of the BLR, the matter redistribution, reverberation eects, and continuum-dependent changes.
A Fetal Well-Being Diagnostic Method Based on Cardiotocographic Morphological Pattern Utilizing Autoencoder and Recursive Feature Elimination

Cardiotocography (CTG), which measures the fetal heart rate (FHR) and maternal uterine contractions (UC) simultaneously, is used for monitoring fetal well-being during delivery or antenatally at the third trimester. Baseline FHR and its response to uterine contractions can be used to diagnose fetal distress, which may necessitate therapeutic intervention. In this study, a machine learning model based on feature extraction (autoencoder), feature selection (recursive feature elimination), and Bayesian optimization, was proposed to diagnose and classify the different conditions of fetuses (Normal, Suspect, Pathologic) along with the CTG morphological patterns. The model was evaluated on a publicly available CTG dataset. This research also addressed the imbalance nature of the CTG dataset. The proposed model has a potential application as a decision support tool to manage pregnancies. The proposed model resulted in good performance analysis metrics. Using this model with Random Forest resulted in a model accuracy of 96.62% for fetal status classification and 94.96% for CTG morphological pattern classification. In rational terms, the model was able to accurately predict 98% Suspect cases and 98.6% Pathologic cases in the dataset. The combination of predicting and classifying fetal status as well as the CTG morphological patterns shows potential in monitoring high-risk pregnancies.
High rate sapphire etching using BCl3-based inductively coupled plasmas

Sapphire was etched in an inductively coupled plasma equipment using BCl3-based gas combination. The etch characteristics of sapphire were investigated as functions of additive gas, inductive power, and dc bias voltage. In the case of additive gas eects, 380 nm/min of etch rate could be obtained in BCl3/Cl2 and the anisotropic etch prole could be observed in BCl 3/HBr. From the ion saturation currents measured by Langmuir probe and optical emission intensities by OES, sapphire etch characteristics appeared to be controlled by the BCl radicals in the plasma. The eects of inductive power and dc bias voltage on the sapphire etch rate were studied in BCl3/HBr/Ar plasmas. The results showed that the increase of inductive power and dc bias voltage increased the sapphire and photoresist etch rates almost linearly. The obtained highest sapphire etch rate in the BCl3/HBr/Ar plasma was 550 nm/min with about 0.87 of the etch selectivity over photoresist and 75 degree of etch prole
Effect of Maltodextrin on Drying Rate of Avocado (Persea Americana Mill.) Pulp by Refractance Window Technique, and on Color and Functional Properties of Powder

This study investigated the effects of various maltodextrin contents (0, 6, 7.5, 9, and 10.5 g/100 g of pulp) on the drying rate and powder quality of avocado pulp dried by refractance window technique. Increasing the maltodextrin addition level significantly decreased the drying rate and prolonged the drying time. The maltodextrin considerably affected the drying rate of pulp when its moisture content was higher than 1.0 g/g dry weight. A significant improvement was found in the retention of total contents of phenolics and chlorophylls, antioxidant capacity, and color in avocado powder obtained from pulp with maltodextrin. The addition of 9 g maltodextrin/100 g of pulp allowed to retain 85.60% TPC, 78.78% total chlorophyll content, 83.48% DPPH radical scavenging activity, and 78.89% ferric reducing antioxidant power. However, maltodextrin in the amount of 10.5 g/100 g of pulp had a negative impact on moisture removal and deteriorated the quality of the dried avocado. These findings may have practical application for the production of dried avocado and other fruits.
Predicting extreme events in the stock market using generative adversarial networks

Accurately predicting extreme stock market fluctuations at the right time will allow traders and investors to make better-informed investment decisions and practice more efficient financial risk management. However, extreme stock market events are particularly hard to model because of their scarce and erratic nature. Moreover, strong trading strategies, market stress tests, and portfolio optimization largely rely on sound data. While the application of generative adversarial networks (GANs) for stock forecasting has been an active area of research, there is still a gap in the literature on using GANs for extreme market movement prediction and simulation. In this study, we proposed a framework based on GANs to efficiently model stock prices’ extreme movements. By creating synthetic real-looking data, the framework simulated multiple possible market-evolution scenarios, which can be used to improve the forecasting quality of future market variations. The fidelity and predictive power of the generated data were tested by quantitative and qualitative metrics. Our experimental results on S&P 500 and five emerging market stock data show that the proposed framework is capable of producing a realistic time series by recovering important properties from real data. The results presented in this work suggest that the underlying dynamics of extreme stock market variations can be captured efficiently by some state-of-the-art GAN architectures. This conclusion has great practical implications for investors, traders, and corporations willing to anticipate the future trends of their financial assets. The proposed framework can be used as a simulation tool to mimic stock market behaviors.
Transformer-Based Attention Network for In-Vehicle Intrusion Detection

Despite the significant advantages of communication systems between electronic control units, the controller area network (CAN) protocol is vulnerable to attacks owing to its weak security structure. The persistent development of intrusion detection systems (IDS) is geared toward preventing vehicles from being targeted by malicious attacks. Recurrent neural networks (RNNs) have emerged as a prominent approach in this domain, contributing significantly to the evolution of IDS. Nonetheless, RNN-based methods have certain limitations in step-by-step processing. Their feature extraction at any given point in time only relies on the hidden state of previously observed information, possibly resulting in missing features in the context vector. In this paper, we propose a novel multi-class IDS using a transformer-based attention network (TAN) for an in-vehicle CAN bus. Our model builds on the self-attention mechanism, removing RNNs and classifying attacks into multiple categories. Furthermore, the proposed models can detect replay attacks by aggregating sequential CAN IDs. The experimental results indicate that the TAN is more efficient than the baselines for different input data types and datasets. The highlight is that, although sequential CAN IDs are used, our model can identify intrusion messages without requiring message labeling. Finally, by inheriting the advantages of transformers, TAN employs transfer learning to improve the performance of models trained on small data from other car models.
Topology of spin meron pairs in coupled Ni/Fe/Co/Cu(001) disks

The meron is a special topological object that carries only one-half of the topological charge unit. In condensed matter physics, a spin meron corresponds to one-half of a spin skyrmion. As compared to the many fascinating topological properties of skyrmion materials, little is known of the properties of spin merons especially about their formation. It was confirmed only recently that hedgehog merons could exist in pairs with opposite helicities via a spin flux closure. However, it is unclear whether a single hedgehog meron could ever exist by pairing with another type of meron. Using element-resolved magnetic imaging measurements on epitaxial trilayer disks, we show that a spin meron with a full range of helicity, including the hedgehog meron, can be stabilized by pairing with another vortex meron with a fine tuning of the magnetic coupling between the two merons. Furthermore, the meron divergence is fully controlled by the polarity of the vortex meron, independent of the vortex helicity.
Interactivity, the heart and soul of effective learning: The interlink between internet self-efficacy and the creation of an inclusive learning experience

There is a growing recognition that the relationship between digital technologies and education is more complex and multi-faceted than previously conceived. Given the multi-faceted nature of digital education, which involves social and technology elements in the realm of knowledge-acquisition, an epistemic community such as a community of inquiry (CoI) provides an appropriate theoretical perspective to frame the analysis of the interlink between internet self-efficacy and inclusive learning experiences. In recognition of the importance of active participation in the ubiquitous learning environment, the argument focused on the development of a CoI as an intellectual community that provides a structure for educators to implement digital education. Central to this article is the development of the various presences to sustain interaction and reflection in a socio-epistemological orientation approach. Notwithstanding the widely recognised affordances of digital technologies to connect people, this study was conducted to elicit relevant evidence on digital education, to gain insights into the practicality of digital education, to articulate several important research questions within each of the identified affordances, and to provide suggestions for institutions pursuing digital education. Furthermore, there is clear evidence that digital education is intertwined with digital capital and digital inclusion.
The Art of Quranic Memorization: A Meta-Analysis

This study highlights the research trends related to Quranic memorization by retrieving and analyzing articles from online scholarly open-source databases such as Google Scholar and Research Gate. The researchers selected 20 articles from online open-source publications published from 2016 to 2021. Findings from this study may help students, teachers, and tahfiz (memorization) institutions develop effective strategies to ensure successful Quranic memorization. Additionally, the study sheds light on the Quranic memorization framework and addresses the research gap that has yet to be carried out by researchers. The study uses meta-analysis to evaluate each article’s content and determine its subject matter. This analysis is then translated into numerical data, including figures for frequency and percentage. Research findings indicate a high research interest relating to methods of Quranic memorization. However, less research has been conducted on sustaining Quranic memorization. Hence, future research should focus on sustaining Quranic memorization regarding methods, challenges, and motivation. Simply memorizing the Quran is not enough; it is essential to maintain memorization.
A Feasible Approach to Disjunctive Knowledge in Situation Calculus

A Feasible Approach to Disjunctive Knowledge in Situation Calculus Stavros Vassos Master of Science Graduate Department of Computer Science University of Toronto 2005 In this thesis we present LP , a reinterpretation of situation calculus based on intuitions from many-valued logics. The key difference is that the notion of truth is based on the fact that a term is interpreted into a set of objects rather than one single object and equality is interpreted as “possibly equals”. LP is suitable for defining action theories that capture fluent-based disjunctive knowledge, which means that any incomplete knowledge the theory captures is limited to be about the value of one fluent each time. This essentially enforces an independence assumption on the fluents which allows for efficient evaluation mechanisms. We show that like situation calculus a similar regression theorem holds in LP . Furthermore, we prove that LP can be embedded in situation calculus and show how a special form of LP theories can be soundly implemented in Prolog and the agent programming language Indigolog.
Spatiotemporal Clustering of Parking Lots at the City Level for Efficiently Sharing Occupancy Forecasting Models

This study aims to address the challenge of developing accurate and efficient parking occupancy forecasting models at the city level for autonomous vehicles. Although deep learning techniques have been successfully employed to develop such models for individual parking lots, it is a resource-intensive process that requires significant amounts of time and data for each parking lot. To overcome this challenge, we propose a novel two-step clustering technique that groups parking lots based on their spatiotemporal patterns. By identifying the relevant spatial and temporal characteristics of each parking lot (parking profile) and grouping them accordingly, our approach allows for the development of accurate occupancy forecasting models for a set of parking lots, thereby reducing computational costs and improving model transferability. Our models were built and evaluated using real-time parking data. The obtained correlation rates of 86% for the spatial dimension, 96% for the temporal one, and 92% for both demonstrate the effectiveness of the proposed strategy in reducing model deployment costs while improving model applicability and transfer learning across parking lots.