Miracle-dz/UniFAR-FTUs huggingface dataset:
CoSiT: An Agent-based Tool for Training and Awareness to Fight the Covid-19 Spread

Since the beginning of 2020 and following the recommendation of the Emergency Committee, the WHO (World Health Organization) Director General declared that the Covid-19 outbreak constitutes a Public Health Emergency of International Concern. Given the urgency of this outbreak, the international community is mobilizing to find ways to significantly accelerate the development of interventions. These interventions include raising awareness of ethical solutions such as wearing a face mask and respecting social distancing. Unfortunately, these solutions have been criticized and the number of infections and deaths by Covid-19 has only increased because of the lack of respect for these gestures on the one hand, and because of the lack of awareness and training tools on the spread of this disease through simulation packages on the other. To give importance to the respect of these measures, the WHO is going to try to propose to his member states, training and sensitization campaigns on coronavirus through simulation packages, so that the right decisions are taken in time to save lives. Thus, a rigorous analysis of this problem has enabled us to identify three directions for reflection. First, how to propose an IT tool based on these constraints in order to generalize training and awareness for all? Secondly, how to model and simulate these prescribed measures in our current reality? Thirdly, how to make it playful, interactive, and participative so that it is flexible according to the user’s needs? To address these questions, this paper proposes an interactive Agent-Based Model (ABM) describing a pedagogical (training and educational) tool that can help understanding the spread of Covid-19 and then show the impact of the barrier measures recommended by the WHO. The tool implemented is quite simple to use and can help make appropriate and timely decisions to limit the spread of Covid-19 in the population. © 2022,International Journal of Advanced Computer Science and Applications. All Rights Reserved.
Machine Learning Based Solution for an Effective Credit Card Fraud Detection

Credit card utilization has been growing with the emergence of e-trade and different programs that permit bills to be made on-line. However, whilst credit score card is stolen or any fraudulent interest takes place, it effects in monetary issues to the cardboard holders. It additionally reasons problems to the provider of playing cards. Therefore, it's far crucial to have a mechanism to stumble on fraudulent on-line transactions. In this regard, there exists many answers as located withinside the literature. One such technique is to have ancient transactions divided into fraudulent and non-fraudulent transactions. This ought to assist educate classifiers to stumble on or suspect fraud transactions. These answers focused spending conduct of clients so that you can stumble on opportunity of fraud. In the present device, statistics mining method is accompanied with random forests to version the conduct of ordinary and fraudulent transactions for credit score card fraud detection. The trouble with the version is that it really works best with dataset this is best tuned. If dataset isn't always good, its overall performance is deteriorated. To conquer this trouble, on this project, a characteristic choice set of rules is proposed to beautify the overall performance of classifier. The proposed device additionally could have comparative have a look at with more than one classifiers like Random Forests and SVM to assess the characteristic choice technique
Contingent Reasoning and Dynamic Public Goods Provision ∗

Contributions toward funding a public good often reveal information that is useful to others who are considering their own contributions. This experiment compares static and dynamic contribution decisions to determine how hypothetical contingent reasoning diﬀers in dynamic decisions where equilibrium requires understanding future information can inform about prior events. We ﬁt a structural model that identi-ﬁes three main types of individuals: A Nash type who is eﬀective at contingent thinking; a partially cursed type who extracts only partial information from contingent events; and a type who is better at extracting information from past, rather than future or concurrent, events.
Large Scale Cloud Deployment of Spectral Topic Modeling

Spectral methods have been employed in a vast majority of use cases which involve discovering the latent factors of a given distribution. Topic Modeling via Latent Dirichlet Allocation (LDA) is one such use case where the goal is to learn the underlying topics or categories from a collection of documents in an unsupervised manner. Spectral LDA using tensor decomposition involves constructing and decomposing a third-order tensor which can be computationally intensive. We describe the design challenges encountered in developing this new approach to topic modeling within the SageMaker framework, the implementation details of the spectral algorithm, and the performance and accuracy measurements compared against existing topic modeling software. We also show that our implementation yields atleast 10x speed improvement with a competitive accuracy over state-of-art open source topic modeling software.
Facial Age Estimation Using Deep Learning: A Review

— In recent times, there have been a surge in the number of researchers, adopting ways and methods in which Artificial Intelligence (AI) can be achieved even with better accuracy than humans and previous handcrafted methods by using deep learning due to its superior performance. Facial age estimation is quite a difficult one and not until recent years that quality results are being obtained by using Deep Learning; and hence it is becoming a crucial system for biometric security framework and has a host of other applications in the real-world. The use of deeply learned Convolutional Neural Networks (CNNs) algorithms has aided in enhancing the accuracy of automatic facial age estimation. In this paper, an in-depth review of research efforts on facial age estimation using deep learning is carried out. Details on other handcrafted techniques adopted by Researchers till date for automatic facial age estimation are also included in order to give an overall idea to interested Researchers. CNNs and their models are reviewed; data sets used, major contributions, performance evaluation metrics, their results and future areas of research in this field as well as challenges are reported.
Effects of Wearing Spandex Pants on Impact Forces and Muscle Activities during Drop Landing

INTRODUCTION: Nigg and Wakeling (2001) proposed that repetitive impact force are not an important form an injury perspective but are the reason for change in muscle activity to minimize soft tissue vibrations. Doan et al. (2003) found that wearing compressive shorts reduced muscle oscillation on landing and concluded that this may have benefit in terms of reduced tissue injury. If wearing spandex pants may tune the muscle to minimize vibration, there is specific adjustment made to reduce joint loading because subsequent changes in muscle activity would change joint stiffness. Since actual effect of wearing spandex pants on muscle activity and impact force are not established, the purpose of this study were to determine how spandex pants affect impact force and muscle activities in the lower extremity.
Pain Management During Labor and Delivery in a Patient with Possible Local Anesthetic Resistance: A Case Report

Local anesthetic resistance is a clinical entity characterized by inadequate analgesia despite technically well-performed procedures. The exact etiology and pathogenesis of this condition are not yet fully understood. A 36-year-old Caucasian female presented to labor and delivery for induction of labor. On admission, the patient reported failure of epidural anesthesia during the previous delivery. An epidural catheter was placed, and analgesia was reported only at high doses of local anesthetic. The patient’s maximum pain level during delivery never reached a score of 2 out of 10. The most common causes of regional anesthetic failure are technical or placement failure, failure related to the local anesthetic itself, or localized infection. This patient appeared to have a true local anesthetic resistance, which was overcome by doubling the customary concentration of local anesthetic. Atypical responses to local anesthetics observed in the patient may be due to incomplete penetrance mutations in sodium channels since local anesthetics work through blocking nerve conduction by acting on these channels.
Correlation of hypothyroidism and reproductive health problems: A cross sectional study

--- Background: Thyroid dysfunction common in women than in men. Hypothyroidism among women of reproductive age group is linked to menstrual irregularities, polycystic ovarian syndrome (PCOS), miscarriage and infertility. Several studies are available in the literature reporting regarding impact of hypothyroidism on one or the other isolated problems of reproductive health problems. Objectives: This study is undertaken to correlate hypothyroidism and reproductive health problems like menstrual irregularities, infertility, PCOS, recurrent pregnancy loss. Materials & Methods: Study Population: All women of age between 18-45 years with reproductive health problems like menstrual irregularities, infertility, PCOS, early pregnancy loss, recurrent pregnancy loss attending Gynaecology OPD in Vinayaka Mission’s problems like menstrual irregularities, PCOS, miscarriages and infertility, thyroid function test should be routinely recommended for these women. Thyroid dysfunction can be corrected with simple, cost-effective treatment. This will help in improvement in pregnancy outcome and also avoid subjecting women for major surgeries for menorrhagia.
M elatonin application on phytocheMical coMpositions of A gAstAche foeniculum under water - deficit stress

Background : Agastache foeniculum is one of the perennial aromatic plants of the Lamiaceae family. The essential oil (EO) of this plant has potent antibacterial and antifungal properties, and hence it is extensively used in the pharmaceutical and food industries. Water-deficit stress occurring at any plant growth stage changes the morphological, phytochemical, and physiological processes. Melatonin is considered as a new kind of plant growth regulator which increases plant tolerance to environmental stresses. Hypotheses: The response to water deficit in A. foeniculum is Mel-dependent and affects their physiological and phytochemical characteristics Studied species: Agastache foeniculum Methods: For this purpose, plants were subjected to water-deficit stress, which was followed by foliar application of melatonin as biostimulants on dry matter and secondary metabolites of A. foeniculum . Results: Results indicated that water-deficit stress (at 60 % of Field capacity) increased shoot dry weight, total phenol, anthocyanins and flavo noids content as well as EO content. Application of various melatonin concentrations improved the content of the mentioned traits. According to GC-MS analysis, ten chemical compounds were recognized in the EO composition of A. foeniculum . The most important EO compound (%) in the studied treatments was methyl chavicol, under severe water-deficit stress conditions with Mel at concentration of 100 μM. Conclusions: The results indicated that the application of Mel (at 100 µM) as a bio-inducer can alleviate the detrimental effects of water-deficit stress (at 60 and 30 % field capacity) and improve the quality of EO.
The Star Formation Main Sequence in the Hubble Space Telescope Frontier Fields

We investigate the relation between star formation rate (SFR) and stellar mass (M), i.e., the main sequence (MS) relation of star-forming galaxies, at in the first four Hubble Space Telescope (HST) Frontier Fields, on the basis of rest-frame UV observations. Gravitational lensing combined with deep HST observations allows us to extend the analysis of the MS down to at and at higher redshifts, a factor of ∼10 below most previous results. We perform an accurate simulation to take into account the effect of observational uncertainties and correct for the Eddington bias. This step allows us to reliably measure the MS and in particular its slope. While the normalization increases with redshift, we fit an unevolving and approximately linear slope. We nicely extend to lower masses the results of brighter surveys. Thanks to the large dynamic range in mass and by making use of the simulation, we analyzed any possible mass dependence of the dispersion around the MS. We find tentative evidence that the scatter decreases with increasing mass, suggesting a larger variety of star formation histories in low-mass galaxies. This trend agrees with theoretical predictions and is explained as either a consequence of the smaller number of progenitors of low-mass galaxies in a hierarchical scenario and/or of the efficient but intermittent stellar feedback processes in low-mass halos. Finally, we observe an increase in the SFR per unit stellar mass with redshift milder than predicted by theoretical models, implying a still incomplete understanding of the processes responsible for galaxy growth.
Sri Lankan Sign Language to Sinhala Text using Convolutional Neural Network Combined with Scale Invariant Feature Transform (SIFT)

— Sri Lankan sign language (SSL) is a visual-gestural language used by the deaf community for communication. Hearing-impaired people cannot effectively communicate with a normal person due to the difficulty in understanding sign language. SSL to Sinhala text interpreting technology using gesture recognition helps to fill up this communication gap since Sinhala is the majority language used in Sri Lanka. Hand gesture recognition can be achieved by using vision-based or sensor-based approaches. Vision-based approaches are comparatively simple and less costly but sensor-based approaches are complex. Scale, rotation, occlusion affects the accuracy of gesture recognition, and keypoints act as better features to handle them. The research focuses on a combined approach of convolutional neural network (CNN) and Scale Invariant Feature Transform (SIFT) to develop a camera-based low-cost solution to interpret static gestures of SSL into Sinhala text. The SSL to Sinhala text translation model reached an accuracy of 86.5% when a dataset of images of 20 static SSL gestures was used. The classifier showed robustness to scale variations when the distance to the camera was varied and uniform color backgrounds were used. Further improvements can be done for the recognition of dynamic gestures and facial expressions of SSL.
HUBBLE SPACE TELESCOPE OBSERVATIONS OF SUB-DAMPED Lyα ABSORBERS AT z < 0.5, AND IMPLICATIONS FOR GALAXY CHEMICAL EVOLUTION

We report observations of four sub-damped Lyα (sub-DLA) quasar absorbers at z < 0.5 ? > obtained with the Hubble Space Telescope Cosmic Origins Spectrograph. We measure the available neutrals or ions of C, N, O, Si, P, S, Ar, Mn, Fe, and/or Ni. Our data have doubled the sub-DLA metallicity samples at z < 0.5 ? > and improved constraints on sub-DLA chemical evolution. All four of our sub-DLAs are consistent with near-solar or super-solar metallicities and relatively modest ionization corrections; observations of more lines and detailed modeling will help to verify this. Combining our data with measurements from the literature, we confirm previous suggestions that the N(H i)-weighted mean metallicity of sub-DLAs exceeds that of DLAs at all redshifts studied, even after making ionization corrections for sub-DLAs. The absorber toward PHL 1598 shows significant dust depletion. The absorbers toward PHL 1226 and PKS 0439-433 show the S/P ratio consistent with solar, i.e., they lack a profound odd–even effect. The absorber toward Q0439-433 shows super-solar Mn/Fe. For several sub-DLAs at z < 0.5 ? > , [N/S] is below the level expected for secondary N production, suggesting a delay in the release of the secondary N or a tertiary N production mechanism. We constrain the electron density using Si ii* and C ii* absorption. We also report different metallicity versus Δ V 90 ? > relations for sub-DLAs and DLAs. For two sub-DLAs with detections of emission lines from the underlying galaxies, our measurements of the absorption-line metallicities are consistent with the emission-line metallicities, suggesting that metallicity gradients are not significant in these galaxies.
AN ANALYSIS OF FACTORS INFLUENCING THE PRICE OF BITCOIN

This paper analyzes the various factors that affect Bitcoin’s price through the application of the VAR model and a variance decomposition analysis. The results of the analysis show that both the transaction demand and speculative demand have a signifcant effect on Bitcoin’s price. Through a variance decomposition analysis, this paper concludes that the impact of speculative demand on Bitcoin’s price is greater than the impact of transaction demand. This indicates that Bitcoin is more inclined to be a speculative commodity.
The Seven Lakes of San Pablo: Assessment and Monitoring Strategies Toward Sustainable Lake Ecosystems

he Seven Lakes of San Pablo City in Laguna, Philippines, provide ecosystem services such as freshwater supply, food, aquaculture, and tourism for the locals and tourists. Due to its vast natural resources, there has been an increase in aquaculture, agriculture, urban settlements, and tourism activities in the lakes in recent years. Realizing the effects of these anthropogenic activities, a comprehensive monitoring effort should be in place to formulate a more holistic approach to sustainable lake management. This review paper summarizes the past and current monitoring and research activities conducted in the Seven Lakes of San Pablo City. While the quarterly monitoring efforts of the lakes’ water quality conducted by the Laguna Lake Development Authority remain necessary, there is a need to employ a more holistic Ecosystem Approach which includes understanding the biological organization which encompasses the essential processes, functions and interactions among the organisms and their environment, and includes the analysis of the role of human society as an integral part of the ecosystem. This includes monitoring the spatial and temporal diversity patterns of native and introduced species, documenting the presence of endocrine disruptors in freshwater fishes currently cultivated in the lakes, identifying the potential risk factors of waterborne parasites contributing to contamination, and generating models for the lakes’ recreational and aquaculture carrying capacity in future monitoring and research efforts. Ecosystem Approach to lake management is proposed, integrating monitoring activities on biophysical dimensions with the socio-economic aspects and stakeholders’ participation to promote sustainable development, equity, and interlinked social-ecological resilience systems. carrying capacity estimation and implications, climate change and pollution, and introduction, waterborne
Ensemble Fake Profile Detection Using Machine Learning (ML)

: The social network, a crucial part of our life is plagued by online impersonation and fake accounts. According to the ‘Community Standards Enforcement Report’ published by Facebook on March 2018, about 583 million fake accounts were taken down just in quarter 1 of 2018 and as many as 3-4% of its active accounts during this time were still fake. In this project, we propose a model that could be used to classify an account as fake or genuine. This model uses Support Vector Machine (SVM) as a classification technique and can process a large dataset of accounts at once, eliminating the need to evaluate each account manually. The community of concern to us here is Fake Accounts and our problem can be said to be a classification or a clustering problem.
The best approximation properties and error bounds of Gaussian networks

The best approximation of any C/sup 2/ function with support on the unit hypercube I/sub m/ in R/sup m/ is considered in the present paper. We prove that a Gaussian radial basis network with centers defined on a regular mesh in R/sup m/ has the best approximation property. Moreover, an upper bound (O(N/sup -2/)) of the approximation is obtained for a network having N/sup m/ units. <<ETX>>
The star formation history in the far outer disc of M33

The outer regions of disc galaxies are becoming increasingly recognized as key testing sites for models of disc assembly and evolution. Important issues are the epoch at which the bulk of the stars in these regions formed and how discs grow radially over time. To address these issues, we use Hubble Space Telescope Advanced Camera for Surveys imaging to study the star formation history (SFH) of two fields at 9.1 and 11.6 kpc along M33’s northernmajor axis. These fields lie at ∼4 and 5 V-band disc scalelengths and straddle the break in M33’s surface brightness profile. The colour–magnitude diagrams (CMDs) reach the ancient main-sequence turn-off with a signal-to-noise ratio of ∼5. From detailed modelling of the CMDs, we find that the majority of stars in both fields combined formed at z < 1. The mean age in the inner field, S1, is ∼3 ± 1 Gyr and the mean metallicity is [M/H]∼−0.5 ± 0.2 dex. The SFH of S1 unambiguously reveals how the inside-out growth previously measured for M33’s inner disc out to∼6 kpc extends out to the disc edge at∼9 kpc. In comparison, the outer field, S2, is older (mean age ∼7±2 Gyr), more metal-poor (mean [M/H]∼−0.8±0.3 dex), and contains ∼30 times less stellar mass. These results provide the most compelling evidence yet that M33’s age gradient reverses at large radii near the disc break and that this reversal is accompanied by a break in stellar mass surface density. We discuss several possible interpretations of this behaviour including radial stellar mixing, warping of the gaseous disc, a change in star formation efficiency and a transition to another structural component. These results offer one of the most detailed views yet of the peripheral regions of any disc galaxy and provide a much needed observational constraint on the last major epoch of star formation in the outer disc.
A perturbative expansion of the evolution operator associated with time-dependent quantum Hamiltonians

A novel expansion -- which generalizes Magnus expansion -- of the evolution operator associated with a (in general, time-dependent) perturbed Hamiltonian is introduced. It is shown that it has a wide range of possible solutions that can be fitted according to computational convenience. The time-independent and the adiabatic case are studied in detail.
On active region loops: Hinode/EIS observations

We have carried out a study of active region loops using observations from the Extreme-ultraviolet Imaging Spectrometer (EIS) on board Hinode using 1 ′′ raster data for an active region observed on May 19, 2007. We find that active region structures which are clearly discernible in cooler lines (� 1MK) become ’fuzzy’ at higher temperatures (� 2MK). The active region was comprised of red-shifted emissions (downflows) in the core and blue-shifted emissions (upflows) at the boundary. The flow velocities estimated in two regions located near the foot points of coronal loop showed red-shifted emission at transition region temperature and blue shifted emission at coronal temperature. The upflow speed in these