Miracle-dz/UniFAR-FTUs huggingface dataset:
PRAGMATIC APPROACH FOR DIGITAL DATA CLUSTERING USING I-DELEGATE ALGORITHM

Now a day, we use all new scientific and technical process in digital world to create huge digital documents so, the analysis of such huge set of document is really difficult and more important work. Document clustering should be an automatic process where documents are partition into clusters having high likeness based on input term. It is a popularly studied problem in text classification but generally the study of analogy measure for document clustering is not based on keywords normally domain based clustering is done. Our main aim is to improve the accessibility, scalability and usability of text mining for various applications. So, to do text document analysis within a stipulated time is a key factor. So it’s not an easy work for examiner to do such analysis in quick period of time. That’s why to do the digital document analysis within less period of time, requires particular techniques to make such difficult task in a simpler way. Such special technique called document clustering. So, clustering algorithms are of great advantage. Here we proposed a I-Delegate algorithm which uses Jaccard distance measure for computing the most dissimilar k documents as centroids for k clusters. Our pragmatic experimental results display that our implemented I-delegate algorithm with Jaccard distance measure for computing the centroid improves the clustering performance of the simple K-means algorithm. The accuracy of clustering of documents has been improved by means of this I-delegate approach due to synonym identification and delegates that synonym to clustering process along with near index approach for better result. Keywords— Documents clustering; I-delegate Algorithm; Jaccard similarity coefficient; K-Means Algorithm
Fuzzy Classifier for Star-Galaxy Separation

The sizes of astronomical surveys are increasing rapidly. Hence, the automatic classification of objects is growing more important. This classification is traditionally based, e.g., on point-spread function fitting. Recently several different neural network approaches have been introduced. In this paper we introduce a simple method that is based on fuzzy set reasoning. The analysis presented here concentrates on separating point sources (stars) from extended ones. The tests show that the neural network approach is superior if compared to direct fuzzy classification. The paper shows that the inherent ability of neural networks to process complex nonlinear data justifies the use of them in astronomical classification. However, a combined fuzzy and neural network approach can be useful at least in special cases.
Dynamics in filled elastomers and consequences

Model filled elastomers are studied in order to relate their microscopic structure to their macroscopic mechanical behavior. First, the polymer dynamics is studied by NMR and we highlight the existence of a gradient glass transition temperature around the solid fillers. We interpret the behavior of several samples at different temperatures with only two parameters: the thickness due to the grafters and the characteristic size of the gradient. This gradient model is still valid in the presence of solvent, as well as for the interpretation of calorimetric measurements. Moreover, the mechanical properties of the samples are measured in oscillatory shear. In addition to the Payne effect typically observed for reinforced elastomers, a non-harmonic signal is detected. The signal analysis shows the presence of strain-hardening and shearthinning within each cycle of solicitation. By comparison to the glassy bridge reinforcement model, we can attribute this behavior to the kinetics of destruction-rebirth of the glassy bridges during the solicitation. Finally, the arrangement of particles in each sample is determined by reverse Monte Carlo simulations based on neutron scattering measurements. Relating this to the polymer dynamics measurements, we show that there is not only a glassy polymer layer around the particles, but also a polymer fraction that is only slightly slowed-down but that contributes significantly to the non linear mechanical behavior of the samples.
Adaptive Patient Support for Rehabilitation Robots

Zusammenfassung Rehabilitationsroboter unterstützen die Rehabilitation von Patienten mit Bewegungsstörungen aufgrund von Schädigungen des Nervensystems. Neu entwickelte, patientenkooperative Regelungsansätze sollen es diesen Robotern ermöglichen, individuell an die Patienten angepasste, effektivere Trainingseinheiten durchzuführen, als dies bislang möglich war. Dieser Beitrag beschreibt zwei Ansätze zur automatischen Anpassung der Roboterunterstützung: Die iterativ lernende Vorsteuerung ermöglicht die Unterstützung von Bewegungen mit definiertem zeitlichem Ablauf. Das iterativ lernende, konservative Kraftfeld ermöglicht die Unterstützung von Bewegungen mit freiem zeitlichem Ablauf. Das Verhalten beider Verfahren wird an einer Beispielanwendung mit dem Gang-Rehabilitationsroboter Lokomat demonstriert. Summary Rehabilitation robots support rehabilitation of patients with neurological movement disorders. Newly developed patient-cooperative control approaches aim at enabling the robots to perform more effective trainings, which are tailored to individual patients. This paper presents two approaches to automatically adapt robotic support: The iterative learning feedforward control is able to support movements with defined timing. The iterative learning conservative force field can also support movements with free timing. Both approaches are demonstrated in an example application with the gait rehabilitation robot Lokomat.
The Effect of Video Game Experience in Different Genres on Task-Switching Ability

Task-switching is an executive function that refers to an individual’s ability to shift attention between different tasks. Previous studies have examined whether video game players perform better on task-switching experiments compared to non-video game players. This study aimed to add to existing research by examining video game experience on a continuous scale. Other factors that may affect the association between video game experience and task-switching ability were also examined, including the specific video game genre(s) played and the age that an individual began actively playing. Sixty participants from the Rochester Institute of Technology completed a questionnaire assessing video game experience and a task-switching experiment with a predictable and random paradigm. Task-switching performance was measured through switch cost, the price paid in response time for switching from one task to another. The results showed that for both the predictable and random task-switching paradigm: 1) Individuals with more video game experience had smaller switch costs than those with less. 2) Individuals who began actively playing earlier in life had no difference in switch cost compared to those who started playing later. 3) Individuals who more frequently play genres higher in complexity had no difference in switch costs compared to those who play less complex genres. 4) Differences in switch cost were robust to a correction that accounts for differences in baseline response times. These results suggest that individuals who play video games frequently may have practice with mechanics that likely transfer to task-switching performance due to them both requiring similar underlying demands.
A node influence ranking algorithm based on probability walking model

For the limitation that current node influence ranking algorithms can only be applied in a single type of network and the results are inaccurate, an algorithm based on similarity is proposed. When a node is similar to many nodes in the network, it is representative and can be treated as an influential node. Firstly, probability walking model is used to simulate the initiative visit between nodes in different types of networks. Secondly, superposed probabilistic transfer similarity is defined based on the model considering nodes’ inbound and outbound information. Finally, node ranking algorithm is set up using the new similarity measuring method. Experiments show that the algorithm can evaluate different kinds of networks with high accuracy, whether the network is directed or undirected, weighted or unweighted.
Ambulatory Blood Pressure Monitoring in Pregnancy; Does It Influence Our Practice?

Background: Maternalhypertensivedisorderisawell-knownmedicalproblemduringpregnancyandisassociatedwithmorbidity and mortality. Out-of-oﬃce blood pressure (BP) measurement has been widely used but the importance of detecting white coat hypertension (WCH) is still a topic for debate. Objectives: The aim of this study was to determine the prevalence of WCH in high-risk pregnant women. Methods: Weincluded56pregnantwomenwithin-oﬃcehigher-than-normalBPwithoutprevioushistoryof HTNtoassesstheirBP using 24-hour ambulatory blood pressure monitoring (ABPM). Results: The mean age of the participants was 31.61 ± 5.42 years (n = 56) and the majority in the second or third trimesters. Hyper-tensionwasdetectedin55.4% using24-hourABPM.Twenty-ﬁvepatients(44.6%)hadWCH,60% of whomwereinthethirdtrimester. There was a signiﬁcant diﬀerence in the incidence of WCH between women younger than 30 and older individuals (P = 0.041). The mean age was 33.13 ± 5.16 years in patients with true HTN and 29.72 ± 5.22 years in WCH patients (P = 0.018). Conclusions: The prevalence of WCH in pregnancy is noteworthy. Regarding its favorable outcome, this might be a heads-up to avoid unnecessary medication during pregnancy and be concise about deﬁning HTN in this population.
Non-Linear Phase Noise Mitigation Over Systems Using Constellation Shaping

This paper presents a modified soft-decoding strategy, which improves performance in the presence of strong phase noise. This can substantially increase the reach of systems that are severely affected by phase noise, generated by fiber non-linear Kerr effect. This strategy is applied to two different experimental scenarios employing constellation shaping, which is known to generate strong non-linear phase noise. In the first experiment, we show that the strategy significantly improves the performance of probabilistically shaped (PS) 64 quadratic-amplitude modulation (QAM) over low-dispersion fibers. In the second experiment, the strategy is used to optimize the position of the points of a 32-QAM constellation (geometrical shaping). This optimized constellation is then compared to standard 32-QAM and PS 64-QAM over standard single-mode fiber. Also in this case, the modified strategy is able to give significant reach gains.
Pattern similarity analyses of frontoparietal task coding: Individual variation and genetic influences

Pattern similarity analyses are increasingly used to characterize coding properties of brain regions, but relatively few have focused on cognitive control processes in FrontoParietal regions. Here, we use the Human Connectome Project (HCP) N-back task fMRI dataset to examine individual differences and genetic influences on the coding of working memory load (0-back, 2-back) and perceptual category (Face, Place). Participants were grouped into 105 MZ (monozygotic) twin, 78 DZ (dizygotic) twin, 99 non-twin sibling, and 100 unrelated pairs. Activation pattern similarity was used to test the hypothesis that FrontoParietal regions would have higher similarity for same load conditions, while Visual regions would have higher similarity in same perceptual category conditions. Results confirmed this highly robust regional double dissociation in neural coding, which also predicted individual differences in behavioral performance. In pair-based analyses, anatomically-selective genetic relatedness effects were observed: relatedness predicted greater activation pattern similarity in FrontoParietal only for load coding, and in Visual only for perceptual coding. Further, in related pairs, the similarity of load coding in FrontoParietal regions was uniquely associated with behavioral performance. Together, these results highlight the power of task fMRI pattern similarity analyses for detecting key coding and heritability features of brain regions.
EXTREME LEARNING MACHINE FOR CLASSIFICATION OF BRAIN TU- MOR IN 3D MR IMAGES ELM ZA KLASIFIKACIJU TUMORA MOZGA KOD 3D MR SNIMAKA

Extreme Learning machine (ELM) a widely adopted algorithm in machine learning field is proposed for the use of pattern classification model using 3D MRI images for identifying tissue abnormalities in brain histology. The four class classification includes gray matter, white matter, cerebrospinal-fluid and tumor. The 3D MRI assessed by a pathologist indicates the ROI and the images are normalized. Texture features for each of the sub-regions is based on the Run-length Matrix, Co-occurence Matrix, Intensity, Euclidean distance, Gradient vector and neighbourhood statistics. Genetic Algorithm is custom designed to extract and sub-select a decisive optimal bank of features which are then used to model the ELM classifier and best selection of ELM algorithm parameters to handle sparse image data. The algorithm is explored using different activation function and the effect of number of neurons in the hidden layer by using different ratios of the number of features in the training and test data. The ELM classification outperformed in terms of accuracy, sensitivity and specificity as 93.20 %, 91.6 %, and 97.98% for discrimination of brain and pathological tumor tissue classification against stateof-the-art feature extraction methods and classifiers in the literature for publicly available SPL dataset. Sažetak ELM, široko prihvaćen algoritam strojnog učenja se predlaže za korištenje u uzorkovanju pomoću klasifikacijskog modela 3D MRI slika za identifikaciju abnormalnosti tkiva u histologiji mozga. Četiri klase obuhvaćaju sive, bijele tvari, cerebrospinalne tekućine-i tumore. 3D MRI koji ocjenjuje patolog, ukazuje na ROI, a slike su normalizirane. Značajke tekstura za svaku od podregija se temelje na Run-length matrici, ponovnom pojavljivanju matrice, intenzitet, euklidska udaljenost, gradijent vektora i statistike susjedstva. Genetski algoritam je obično dizajniran za izdvajanje i sub-optimalan odabir odlučujući o značajkama koje se onda koriste za model ELM klasifikatora i najbolji izbor ELM parametra algoritama za obradu rijetkih slikovnih podataka. Algoritam se istražuje koristeći različite aktivacijske funkcije i utjecaj broja neurona u skrivenom sloju pomoću različitih omjera broja značajki kod trening i test podataka. ELM klasifikacija je nadmašila u smislu točnosti, osjetljivosti i specifičnosti, kao 93,20%, 91,6% i 97,98% za diskriminaciju mozga i patološki kod tumora i sistematizacije metode za prikupljanje podataka i klasifikatore u literaturi za javno dostupne SPL skup podataka.
The accuracy of selected hematology and inflammatory indices for predicting preterm labor; a cross-sectional study

Introduction: The role of inflammatory indices has been suggested in preterm labor. Objectives: The possible relationship of some inflammatory and hematological markers were investigated as predictors of preterm labor. Patients and Methods: Around 110 full-term and 109 preterm women were randomly enrolled. According to the gestational age at delivery, they were divided into two groups including <37 weeks and ≥37 weeks pregnancies. The patients’ age, body mass index (BMI), history of smoking were recorded. Selected hematological parameters, erythrocyte sedimentation rate (ESR) and high-sensitive C-reactive protein (hsCRP) were measured. Comparison of groups, the odd’s ratio and accuracy of each index were evaluated and assessed statistically. Results: Overall, 110 full-term and 109 preterm mothers were evaluated. Mothers with preterm labor had higher WBC, hemoglobin, neutrophil proportion, neutrophil/lymphocyte ratio (NLR), platelet count, platelet/lymphocyte ratio (PLR), and ESR and also hs-CRP values. However, they had lower lymphocyte proportion than mothers with full-term delivery. Hs-CRP higher than 10.9 mg/L showed moderate sensitivity (50.46%) instead of high specificity (90.91%), while ESR >30 mm revealed 85.32% sensitivity and 93.64% specificity for predicting preterm labor. Lymphocyte ratio ≤22% and NLR >3.21 represented high sensitivity and plausible specificity. Hemoglobin >10.8 g/dL, platelet count >178000 cells/μL, PLR> 8.53 and WBC >7800 cells/μL showed adequate sensitivities with low to moderate specificities to predict preterm labor. Conclusion: Most of the investigated variables were associated with preterm delivery. The cut-off values obtained here may help obstetricians to predict preterm delivery, and therefore to manage its complications, costs, hazards and care problems.
DEPRESSION, ANXIETY, AND STRESS IN RELATION TO LIFE SATISFACTION AND ACADEMIC PERFORMANCE OF ADOLESCENTS

Background: Depression, anxiety, and stress all have number of emotional and physical consequences and can decrease a personâ€™s ability to function at work and at home. Life satisfaction is global satisfaction with individual lives in different life domains. All these mechanisms play their role in adolescentsâ€™ academic performance. Current study intended to explore the relationship of depression, anxiety, and stress with life satisfaction and academic performance of adolescents. Moreover study also aimed to see gender and grade wise differences. Methods: It was a cross sectional study that used convenience sampling approach data collection. Sample comprised of 500 adolescents (age range = 13-21 years). Depression, anxiety and stress scale and Satisfaction with Life Scale were used to assess the study variables. Correlation coefficient among variables was computed to discern the pattern of relationship among variables. Mean depression, anxiety, stress, and life satisfaction levels were compared between the groups using independent t-test at alpha 0.05. Results: findings revealed significant negative relationship of depression, anxiety, and stress with life satisfaction of students. Gender differences revealed significant differences on variables of anxiety and stress. Significant differences in depression, anxiety, stress and life satisfactions of high and low grade achievers were also found. Conclusion: Adolescents who experience depression, anxiety, and stress are less satisfied with their lives and show low academic performance in terms of grades. Moreover gender also effects on vulnerability to experience anxiety and stress. Study also highlights the practical implications for parents, educators, counselors, and mental health professionals.
Dynamical Aquaplanet Experiments with Uniform Thermal Forcing: System Dynamics and Implications for Tropical Cyclone Genesis and Size

Existing hypotheses for the dynamical dependence of tropical cyclone genesis and size on latitude depend principally on the Coriolis parameter f. These hypotheses are tested via dynamical aquaplanet experiments with uniform thermal forcing in which planetary rotation rate and planetary radius are varied relative to Earth values; the control simulation is also compared to a present-day Earth simulation. Storm genesis rate collapses to a quasi-universal dependence on f that attains its maximum at the critical latitude, where the inverse-f scale and Rhines scale are equal. Minimum genesis distance from the equator is set by the equatorial Rhines (or deformation) scale and not by a minimum value of f. Outer storm size qualitatively follows the smaller of the two length scales, including a slow increase with latitude equatorward of 45° in the control simulation, similar to the Earth simulation. The latitude of peak size scales with the critical latitude for varying planetary radius but not rotation rate, possibly because of the dependence of genesis specifically on f. The latitudes of peak size and peak packing density scale closely together. Results suggest that temporal effects and interstorm interaction may be significant for size dynamics. More generally, the critical latitude separates two regimes: 1) a mixed wave–cyclone equatorial belt, where wave effects are strong and the Rhines scale likely limits storm size, and 2) a cyclone-filled polar cap, where wave effects are weak and cyclones persist. The large-planet limit predicts a world nearly covered with long-lived storms, equivalent to the f plane. Overall, spherical geometry is likely important for understanding tropical cyclone genesis and size on Earthlike planets.
What Drives Investor Response to CSR Performance Reports?

Recent research finds that investors' assessments of a stock's fundamental value are influenced by corporate social responsibility (CSR) performance through the affect‐as‐information heuristic. We extend prior research by examining two boundary conditions for the use of this heuristic: (i) whether the CSR performance relates to activities that are integrated in a firm's core business practices (material CSR issues) or not (immaterial CSR issues), and (ii) whether the CSR performance is positive or negative. Employing an experimental method, we find that the affect‐as‐information heuristic applies only to immaterial CSR issues but not to material CSR issues, and only to positive but not negative CSR performance. Our findings suggest that investors likely use a heuristic approach to process immaterial and positive CSR issues, and a more deliberate and systematic approach to process material or negative CSR issues. Our study has both practical and theoretical implications. Qu'est‐ce qui oriente la reaction des investisseurs aux rapports de performance quant a la RSE? Les etudes recentes menent a la conclusion que la determination de la valeur fondamentale d'une action par les investisseurs est soumise a l'influence de la performance quant a la RSE (responsabilite sociale de l'entreprise) telle qu'elle est percue selon une heuristique d'inference arbitraire (affect‐as‐information heuristic) (Elliott et al., 2014). Les auteurs poussent plus loin les recherches precedentes en examinant deux conditions limites d'utilisation de cette heuristique : 1) l'existence d'un lien entre la performance quant a la RSE et les activites qui sont integrees aux pratiques commerciales essentielles d'une entreprise (enjeux d'importance lies a la RSE) ou son inexistence (enjeux de peu d'importance lies a la RSE), et 2) l’evaluation positive ou negative de la performance quant a la RSE. Recourant a une methode experimentale, les auteurs constatent que cette heuristique s'applique uniquement aux enjeux de peu d'importance lies a la RSE et non aux enjeux d'importance, et uniquement a la performance positive quant a la RSE et non a la performance negative. Les observations des auteurs semblent indiquer que les investisseurs sont susceptibles de recourir a une methode heuristique dans le traitement des enjeux de peu d'importance et positifs lies a la RSE, et une methode plus reflechie et systematique dans celui des enjeux d'importance ou negatifs lies a la RSE. Les retombees de l’etude sont a la fois pratiques et theoriques.
The Farrell–Jones conjecture for arbitrary lattices in virtually connected Lie groups

Let G be a cocompact lattice in a virtually connected Lie group or the fundamental group of a 3-manifold. We prove the K-theoretic Farrell-Jones Conjecture (up to dimension one) and the L-theoretic Farrell-Jones Conjecture for G, where we allow coefficients in additive G-categories with (involution).