Miracle-dz/UniFAR-FTUs huggingface dataset:
Mindfulness Based Intervention Program on Anxiety and Depressive Symptoms among Pregnant Women

The aim of this study was to investigate the effect of mindfulness based intervention program on anxiety and depressive symptoms among pregnant women. A Quasi-experimental design was used for this study. This study was conducted at antenatal outpatient clinic at El-Manial Educational University Hospital-Cairo University. A purposive sample consisted of 30 pregnant women with high risk for anxiety and depressive symptoms as confirmed by senior obstetrician according to screening sheet for high risk pregnant women. The sample was divided randomly into two groups; study and control groups (15 each). Three tools were used to collect the data for the present study; a) Personal Data Sheet; b) Edinburgh Postnatal Depression Scale; and c) Perceived Stress Scale. The sample was divided into two groups, fifteen pregnant women as the study group and fifteen pregnant women as control group. Aconstructed mindfulness based intervention program was developed by the researchers and implemented to the study group in ten sessions that were held twice weekly, and each session ranged from 60 to 90 minutes. The main study findings revealed that, there was a statistically significant difference between study and control groups in the reduction of anxiety and depressive symptoms. The study concluded that mindfulness based intervention program was effective with pregnant women concerning the reduction of the anxiety and depression. The study recommended that, there is a great need for performing long-term follow-up in order to evaluate the effects of Mindfulness based intervention program on different pregnancy-related outcomes such as emotional well-being, postnatal depression, and mother-infant relationships.
Evaluation of Recurrent Neural Network Architectures for Error Detection

In this paper we evaluate several neural network architectures for error detection in English. For this task, we employ two different error tagsets based on the First Certificate in English (FCE) corpora: a simple binary tagset (correct or incorrect) and an enriched tagset as utilized by the Cambridge Learner’s Corpus (97 tags). The models we experiment with belong to the widely-adopted recurrent neural network family: Long Short-Term Memory networks (LSTMs) and Gated Recurrent Networks (GRUs). Though the LSTMs perform slightly better on the simple tagset and GRUs similarly on the enriched tagset, our results align with past literature in that there does not seem to be a clear difference between the two. We thus conclude that more training data is required as is a tagset that is simultaneously expressive and balanced.
The Malaria Parasite

student of the question can have any doubt; but when it is held, as seema to be the case by some, that the parasite can only be obtained by the mosquito from man, and can only be conveyed by the mosquito to man, Dr. Hillier thinks, and we think, that the theory goes beyond the proof, and he points to certain facts which appear to militate against it. For instance, as he says, Rhodesia is a huge country of 150,000 square miles, while its entire population is estimated at 300,000 blacks and 15,000 white. Very large portions of the country, therefore, are uninhabited, and in some of these districts the low-lying, swampy ground, where malaria is most virulent? there is in many
Attosecond chirp-encoded dynamics of light nuclei

We study the spectral phase of high-order harmonic emission as an observable for probing ultrafast nuclear dynamics after the ionization of a molecule. Using a strong-field approximation theory that includes nuclear dynamics, we relate the harmonic phase to the phase of the overlap integral of the nuclear wavefunctions of the initial neutral molecule and the molecular ion after an attosecond probe delay. We determine experimentally the group delay of the high harmonic emission from D2 and H2 molecules, which allows us to verify the relation between harmonic frequency and the attosecond delay. The small difference in the harmonic phase between H2 and D2 calculated theoretically is consistent with our experimental results.
Levels of Urine Cotinine from Hookah Smoking and Exposure to Hookah Tobacco Secondhand Smoke in Hookah Lounges and Homes

Background Nicotine, an addictive drug, is present in all forms of tobacco products, including hookah tobacco, which is not yet regulated in the United States. Objectives This study aimed to investigate the uptake of nicotine in hookah smokers and non-smokers exposed to secondhand smoke (SHS) at indoor hookah social events in natural settings where hookah tobacco was smoked exclusively. Patients and Methods We quantified cotinine, a metabolite of nicotine, in the urine of 105 hookah smokers and 103 non-smokers. Participants provided spot urine samples the morning of and the morning after attending an indoor hookah-only smoking social event at a hookah lounge or in a private home. Results Following a social event where exclusively hookah tobacco was smoked, urinary cotinine levels increased significantly 8.5 times (geometric mean (GM): 16.0 ng/mg to 136.1 ng/mg) among hookah smokers, and 2.5 times (GM: 0.4 ng/mg to 1.0 ng/mg) among non-smokers exposed exclusively to hookah tobacco SHS. Among hookah smokers, the highest increase in urinary cotinine levels post a hookah event was found in occasional hookah smokers in which GM levels increased significantly 31.2 times post smoking (from 2.0 ng/mg to 62.3 ng/mg). Reported reasons for preference to smoke hookah at home by hookah smokers who attended a hookah social event in a private home included recreational purposes, socializing with friends and family, ‘Me’ time and relaxing at home, more comfortable to smoke hookah at home, owning a hookah and hookah tobacco, eating and drinking while smoking hookah, and saving money by smoking at home and not going to hookah lounges. Conclusions Hookah tobacco smoke is a source of substantial nicotine exposure. Our results call for protecting hookah smokers’ and non-smokers’ health by requiring accurate hookah tobacco labels, raising taxes on hookah tobacco, reducing the spread of hookah lounges, and encouraging voluntary bans on smoking hookah tobacco in private homes.
A Practical Algorithm for Distributed Clustering and Outlier Detection

We study the classic $k$-means/median clustering, which are fundamental problems in unsupervised learning, in the setting where data are partitioned across multiple sites, and where we are allowed to discard a small portion of the data by labeling them as outliers. We propose a simple approach based on constructing small summary for the original dataset. The proposed method is time and communication efficient, has good approximation guarantees, and can identify the global outliers effectively. To the best of our knowledge, this is the first practical algorithm with theoretical guarantees for distributed clustering with outliers. Our experiments on both real and synthetic data have demonstrated the clear superiority of our algorithm against all the baseline algorithms in almost all metrics.
A novel mouse model of Campylobacter jejuni enteropathy and diarrhea

Campylobacter infections are among the leading bacterial causes of diarrhea and of ‘environmental enteropathy’ (EE) and growth failure worldwide. However, the lack of an inexpensive small animal model of enteric disease with Campylobacter has been a major limitation for understanding its pathogenesis, interventions or vaccine development. We describe a robust standard mouse model that can exhibit reproducible bloody diarrhea or growth failure, depending on the zinc or protein deficient diet and on antibiotic alteration of normal microbiota prior to infection. Zinc deficiency and the use of antibiotics create a niche for Campylobacter infection to establish by narrowing the metabolic flexibility of these mice for pathogen clearance and by promoting intestinal and systemic inflammation. Several biomarkers and intestinal pathology in this model also mimic those seen in human disease. This model provides a novel tool to testing specific hypotheses regarding disease pathogenesis as well as vaccine development that is currently in progress. Author Summary Campylobacter jejuni has been identified as one of the leading causes of enteropathy and diarrhea. In developing countries, these repeated enteric infections often result in growth deficits and cognitive impairment. There is a lack of small animal models of Campylobacter infection. This is a major hurdle in understanding the pathogenesis of Campylobacter infection in order to lead to therapeutic treatments and vaccines. We have developed a highly reproducible mouse model of Campylobacter infection that has clinical outcomes that match those of malnourished children. We hope that these insights into Campylobacter susceptibility will lead to the development of treatments against this major cause of diarrheal illness.
Term Structure ( s ) of Equity Risk Premia

Using dividend and variance swap data simultaneously, we provide a series of novel facts on the two term structures of equity risk premia: holding period returns and maturity. These two dimensions are distinct and jointly important for a better understanting of risk premia and its time variation. We also uncover new information on the level and variation of the term structure of the (physical) expectation of dividend growth. Moreover, we find that liquidity is priced in the cross-section of variance and dividend swaps and affect many conclusions in related literature. Among many facts, we show that: risk premium in increasing and concave on maturity; short-maturity and short-horizon risk premium is lower than market risk premium; risk premium is also increasing and concave on investment horizon, but increases much faster than maturity; independently of maturity, most of the risk premium is associated with short-horizon risk premium; dividends are highly predictable once we account for time variation in term structure of holding period returns; and liquidity is relevant for short-term holding period returns and to explain the high returns of dividend contracts. Using both dimensions jointly highlights additional challenges to asset pricing models, but, unlike previous literature, we show that all these facts are consistent, for instance, with a long-run risk model with disaster risks.
Random forest for credit card fraud detection

Credit card fraud events take place frequently and then result in huge financial losses. Criminals can use some technologies such as Trojan or Phishing to steal the information of other people's credit cards. Therefore, an effictive fraud detection method is important since it can identify a fraud in time when a criminal uses a stolen card to consume. One method is to make full use of the historical transaction data including normal transactions and fraud ones to obtain normal/fraud behavior features based on machine learning techniques, and then utilize these features to check if a transaction is fraud or not. In this paper, two kinds of random forests are used to train the behavior features of normal and abnormal transactions. We make a comparison of the two random forests which are different in their base classifiers, and analyze their performance on credit fraud detection. The data used in our experiments come from an e-commerce company in China.
Moving Least Squares Support Vector Machines for weather temperature prediction

Local learning methods have been investigated by many researchers. While global learning methods consider the same weight for all training points in model ﬁtting, local learning methods assume that the training samples in the test point region are more inﬂuential. In this paper, we propose Moving Least Squares Support Vector Machines (M-LSSVM) in which each training sample is involved in the model ﬁtting depending on the similarity between its feature vector and the one of the test point. The experimental results on an application of weather forecasting indicate that the proposed method can improve the prediction performance.
Fen Öğretiminde Kavram Karikatürü Kullanımının Öğrencilerin Akademik Başarılarına ve Sorgulayıcı Öğrenme Becerileri Algılarına Etkisi

Ulkemizde bilim ve teknolojideki gelismelere ayak uydurabilmek icin fen ogretiminin gerekliliginin farkina varilmis, fen ogretim programinin icerigini ve felsefesinde degistirmeklere donusturuldu. Fen ve Teknoloji egitimi program yapilandirma yaklasimi ve iceriginde ogrencilerin surece aktif katilimini saglamaya yonelik yontemler ve tekniklere yer verilmistir. Yapilandirmaci yaklasima gore ogrencilerin bilgiyi yapilandirma sureinde aktif olmalari gereken acidan ogrenme surumu ogrencileri derse katabilecek, tartisma ortamlari yaratarak bunlar daha anlamli ogrenmelerini saglayabilecek gorsel araclarin kullanimi onem tasimaktadir. Soz konusu araclardan biri de kavram karikaturleridir. Ziyaretteki fen egini kullanilan kavram karikaturleri, ogrencilerin akademik basarilarina ve sorgulayici ogrenme becerileri algilarina etkisi belirlenmeye calisti. Arastirmadan elde edilen bulgulara gore, deney grubu ile kontrol grubu arasinda akademik basari puanlari bakimindan anlamli bir farklilik bulamazken, sorgulayici ogrenme becerileri algi puanlari bakimindan deney grubu lehinde anlamli bir fark bulunmustur. Arastirmadan elde edilmis sonuclara gore, kavram karikaturlerinin ogrencileri var olan deneyimleriyle, yeni karsilastiklari bilgi sorgulamalarina yardimci olarak, ogrencileri bu yondeki algilarini etkiledigi belirlenmistir.
Revenge of the host: cannibalism, ontogenetic niche shifts, and the evolution of life-history strategies in host-parasitoid systems

Question: How does cannibalism in the host alter the evolution of a parasitoid’s oviposition strategy? Can differences in cannibalism risk between parasitized and healthy hosts alter the stage-specific foraging of parasitoids? Can host-specific differences in cannibalistic behaviour explain why parasitoids vary in what host stages they attack? Mathematical methods: We examined the evolutionary dynamics of a stage-structured host–parasitoid model using two complementary approaches: (1) individual-based numerical simulations of evolutionary dynamics, and (2) the theory of adaptive dynamics focusing on evolutionarily stable strategies (ESSs). Assumptions: Cannibalism in the host is assumed to be stage structured, with larger stages consuming smaller stages. The consumption of parasitized hosts also results in killing of the parasitoid’s offspring. Vulnerability to cannibalism of parasitized versus healthy hosts was allowed to vary. The parasitoid’s preference for attacking early versus late host stages was the trait under selection and allowed to evolve. Results: When cannibalism rates increase relative to the parasitoid’s attack rates, the ESS of the parasitoid shifts from attacking only early host stages to attacking only late host stages. This shift occurs at lower cannibalism rates when parasitized hosts are more susceptible to cannibalism than healthy hosts. Under equilibrium conditions, a small boundary area exists between these two regions where attacking only early or only late host stages are alternative ESSs. The threshold and alternative stable ESSs are the result of cannibalism, which creates a positive feedback between the parasitoid’s oviposition rate and its own mortality. Intermediate strategies, where parasitoids evolve to attack both stages, occur only when host populations exhibit large population oscillations or when generalist parasitoids that attack both stages have a foraging advantage.
Instability of a liquid jet emerging from a droplet upon collision with a solid surface

A linear perturbation theory is developed to investigate the interface instabilities of a radially-expanding, liquid jet in cylindrical geometries. The theory is applied to rapidly spreading droplets upon collision with solid surfaces as the fundamental mechanism behind splashing. The analysis is based on the observation that the instability of the liquid sheet, i.e., the formation of the fingers at the spreading front, develops in the extremely early stages of droplet impact. The shape evolution of the interface in the very early stages of spreading is numerically simulated based on the axisymmetric solutions obtained by a theoretical model. The effects that factors such as the transient profile of an interface radius, the perturbation onset time, and the Weber number have on the analysis results are examined. This study shows that a large impact inertia, associated with a high Weber number, promotes interface instability, and prefers high wave number for maximum instability. The numbers of fingers at the spreading front of droplets predicted by the model agree well with those experimentally observed.
Maimonides’ Ladder: States of Mutual Knowledge and the Perception of Charitability

Why do people esteem anonymous charitable giving? We connect normative theories of charitability (captured in Maimonides’ Ladder of Charity) with evolutionary theories of partner choice to test predictions on how attributions of charitability are affected by states of knowledge: whether the identity of the donor or of the beneficiary is revealed to the other. Consistent with the theories, in Experiments 1–2 participants judged a double-blind gift as more charitable than one to a revealed beneficiary, which in turn was judged as more charitable than one from a revealed donor. We also found one exception: Participants judged a donor who revealed only himself as slightly less, rather than more, charitable than one who revealed both identities. Experiment 3 explains the exception as a reaction to the donor’s perceived sense of superiority and disinterest in a social relationship. Experiment 4 found that donors were judged as more charitable when the gift was shared knowledge (each aware of the other’s identity, but unsure of the other’s awareness) than when it was common knowledge (awareness of awareness). Experiment 5, which titrated anonymity against donation size, found that not even a hundredfold larger gift could compensate for the disapproval elicited by a donor revealing his identity. Experiment 6 showed that participants’ judgments of charitability flip depending on whose perspective they take: Observers disapprove of donations that they would prefer as beneficiaries. Together, these experiments provide insight into why people care about how a donor gives, not just how much.
A CONSISTENT NONPARAMETRIC TEST ON SEMIPARAMETRIC SMOOTH COEFFICIENT MODELS WITH INTEGRATED TIME SERIES

In this paper, we propose a simple nonparametric test for testing the null hypothesis of constant coefficients against nonparametric smooth coefficients in a semiparametric varying coefficient model with integrated time series. We establish the asymptotic distributions of the proposed test statistic under both null and alternative hypotheses. Moreover, we derive a central limit theorem for a degenerate second order U-statistic, which contains a mixture of stationary and nonstationary variables and is weighted locally on a stationary variable. This result is of independent interest and useful in other applications. Monte Carlo simulations are conducted to examine the finite sample performance of the proposed test.
Expressed exome capture sequencing: A method for cost‐effective exome sequencing for all organisms

Exome capture is an effective tool for surveying the genome for loci under selection. However, traditional methods require annotated genomic resources. Here, we present a method for creating cDNA probes from expressed mRNA, which are then used to enrich and capture genomic DNA for exon regions. This approach, called “EecSeq,” eliminates the need for costly probe design and synthesis. We tested EecSeq in the eastern oyster, Crassostrea virginica, using a controlled exposure experiment. Four adult oysters were heat shocked at 36°C for 1 hr along with four control oysters kept at 14°C. Stranded mRNA libraries were prepared for two individuals from each treatment and pooled. Half of the combined library was used for probe synthesis, and half was sequenced to evaluate capture efficiency. Genomic DNA was extracted from all individuals, enriched via captured probes, and sequenced directly. We found that EecSeq had an average capture sensitivity of 86.8% across all known exons and had over 99.4% sensitivity for exons with detectable levels of expression in the mRNA library. For all mapped reads, over 47.9% mapped to exons and 37.0% mapped to expressed targets, which is similar to previously published exon capture studies. EecSeq displayed relatively even coverage within exons (i.e., minor “edge effects”) and even coverage across exon GC content. We discovered 5,951 SNPs with a minimum average coverage of 80×, with 3,508 SNPs appearing in exonic regions. We show that EecSeq provides comparable, if not superior, specificity and capture efficiency compared to costly, traditional methods.
Hierarchical Discrete Event Systems with Inputs and Outputs

We propose a framework for the hierarchical design of discrete event systems that addresses both safety and liveness properties. Technically, we build on a notion of inputs and outputs that is closely related to J.C. Willems' behavioural systems theory. We develop a structural admissibility condition that allows for abstraction-based controller synthesis similar to previous work on hybrid control systems. A key feature of our framework is an alternation of subsystem composition and controller synthesis that is expected to be computationally efficient whenever the complexity of the safety specifications is independent of the respective layer in the hierarchy
Multi-Task Zipping via Layer-wise Neuron Sharing

Future mobile devices are anticipated to perceive, understand and react to the world on their own by running multiple correlated deep neural networks on-device. Yet the complexity of these neural networks needs to be trimmed down both within-model and cross-model to fit in mobile storage and memory. Previous studies focus on squeezing the redundancy within a single neural network. In this work, we aim to reduce the redundancy across multiple models. We propose Multi-Task Zipping (MTZ), a framework to automatically merge correlated, pre-trained deep neural networks for cross-model compression. Central in MTZ is a layer-wise neuron sharing and incoming weight updating scheme that induces a minimal change in the error function. MTZ inherits information from each model and demands light retraining to re-boost the accuracy of individual tasks. Evaluations show that MTZ is able to fully merge the hidden layers of two VGG-16 networks with a 3.18% increase in the test error averaged on ImageNet and CelebA, or share 39.61% parameters between the two networks with <0.5% increase in the test errors for both tasks. The number of iterations to retrain the combined network is at least 17.8 times lower than that of training a single VGG-16 network. Moreover, experiments show that MTZ is also able to effectively merge multiple residual networks.
Improved adsorption energetics within density-functional theory using revised Perdew-Burke-Ernzerhof functionals

A simple formulation of a generalized gradient approximation for the exchange and correlation energy of electrons has been proposed by Perdew, Burke, and Ernzerhof (PBE) [Phys. Rev. Lett. 77, 3865 (1996)]. Subsequently Zhang and Yang [Phys. Rev. Lett. 80, 890 (1998)] have shown that a slight revision of the PBE functional systematically improves the atomization energies for a large database of small molecules. In the present work, we show that the Zhang and Yang functional (revPBE) also improves the chemisorption energetics of atoms and molecules on transition-metal surfaces. Our test systems comprise atomic and molecular adsorption of oxygen, CO, and NO on Ni(100), Ni(111), Rh(100), Pd(100), and Pd(111) surfaces. As the revPBE functional may locally violate the Lieb-Oxford criterion, we further develop an alternative revision of the PBE functional, RPBE, which gives the same improvement of the chemisorption energies as the revPBE functional at the same time as it fulfills the Lieb-Oxford criterion locally.