Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonmedical Determinants of Congenital Heart Diseases in Children from the Perspective of Mothers: A Qualitative Study in Iran

Introduction Mortality due to noncommunicable diseases has increased in the world today with the advent of demographic shifts, growing age, and lifestyle patterns in the world, which have been affected by economic and social crises. Congenital heart defects are one of the forms of diseases that have raised infant mortality worldwide. The objective of present study was to identify nonmedical determinants related to this abnormality from the mother's perspectives. Methods This research was a qualitative study and the data collection method was a semistructured interview with mothers who had children with congenital heart diseases referring to the Shahid Rajaei Heart Hospital in Tehran, Iran. A thematic analysis approach was employed to analyze transcribed documents assisted by MAXQDA Plus version 12. Results Four general themes and ten subthemes including social contexts (social harms, social interactions, and social necessities), psychological contexts (mood disorders and mental well-being), cultural contexts (unhealthy lifestyle, family culture, and poor parental health behaviors), and environmental contexts (living area and polluted air) were extracted from interviews with mothers of children with congenital heart diseases. Conclusions Results suggest that factors such as childhood poverty, lack of parental awareness of congenital diseases, lack of proper nutrition and health facilities, education, and lack of medical supervision during pregnancy were most related with the birth of children with congenital heart disease from mothers' prospective. In this regard, targeted and intersectorial collaborations are proposed to address nonmedical determinants related to the incidence of congenital heart diseases.
Dropout against Deep Leakage from Gradients

As the scale and size of the data increases significantly nowadays, federal learning (Bonawitz et al. [2019]) for high performance computing and machine learning has been much more important than ever before (Abadi et al. [2016]). People used to believe that sharing gradients seems to be safe to conceal the local training data during the training stage. However, Zhu et al. [2019] demonstrated that it was possible to recover raw data from the model training data by detecting gradients. They use generated random dummy data and minimise the distance between them and real data. Zhao et al. [2020] pushes the convergence algorithm even further. By replacing the original loss function with cross entropy loss, they achieve better fidelity threshold. In this paper, we propose using an additional dropout (Srivastava et al. [2014]) layer before feeding the data to the classifier. It is very effective in preventing leakage of raw data, as the training data cannot converge to a small RMSE even after 5,800 epochs with dropout rate set to 0.5.
Synthesis and characterization of alkali-metal reduced polycyclic aromatic hydrocarbon based materials

The reduction chemistry of polycyclic aromatic hydrocarbon (PAH) based materials is well established and has been investigated for more than a century. A frequently applied reduction method is treatment of the PAH by alkali metals, where an electron from the alkali metal is transferred to the π-system of the PAH. Recently it has been found that these reduced species can possess intriguing magnetic and electronic properties in the solid state, e.g. pentacene derivatives are excellent organic semiconductors and triphenylides are low-dimensional antiferromagnets. This topic has also attracted considerable attention since superconductivity was reported in alkali-metal reduced PAHs synthesised by solid-state techniques. However, the unavailability of well-crystalline and single-phase materials, which would permit the unambiguous identification of the superconducting phase and disclose the origin of superconductivity, has severely hindered progress in this research direction. In this thesis the reduction of PAHs with potassium, rubidium and caesium metals was carried out in aprotic coordinating organic solvents. Single crystalline and polycrystalline phase-pure, solvent containing- and solvent-free reduction products were successfully isolated for the first time. Crystal structures were determined with single-crystal and powder X-ray diffraction, magnetism was studied with Superconducting Quantum Interference Device (SQUID) magnetometry and electron paramagnetic resonance (EPR) spectroscopy, and charge transfer was followed with infrared (IR) and Raman spectroscopies. The synthesised materials exhibit a variety of magnetic properties, spanning canted-antiferromagnetism, ferromagnetism, low-dimensional magnetism and diamagnetism. A detailed structural analysis was performed in order to establish magnetostructural correlations. This study has expanded the family of well characterised alkali-metal reduced PAH based materials and gives an insight into the structures and magnetism of this family of compounds.
Understanding of Kernels in CNN Models by Suppressing Irrelevant Visual Features in Images

Deep learning models have shown their superior performance in various vision tasks. However, the lack of precisely interpreting kernels in convolutional neural networks (CNNs) is becoming one main obstacle to wide applications of deep learning models in real scenarios. Although existing interpretation methods may find certain visual patterns which are associated with the activation of a specific kernel, those visual patterns may not be specific or comprehensive enough for interpretation of a specific activation of kernel of interest. In this paper, a simple yet effective optimization method is proposed to interpret the activation of any kernel of interest in CNN models. The basic idea is to simultaneously preserve the activation of the specific kernel and suppress the activation of all other kernels at the same layer. In this way, only visual information relevant to the activation of the specific kernel is remained in the input. Consistent visual information from multiple modified inputs would help users understand what kind of features are specifically associated with specific kernel. Comprehensive evaluation shows that the proposed method can help better interpret activation of specific kernels than widely used methods, even when two kernels have very similar activation regions from the same input image.
Constraints on the Occurrence of ‘Oumuamua-Like Objects

At present, there exists no consensus in the astronomical community regarding either the bulk composition or the formation mechanism for the interstellar object 1I/2017 U1 (‘Oumuamua). With the goal of assessing the merits of the various scenarios that have been suggested to explain ‘Oumuamua's appearance and observed properties, we report a number of new analyses and provide an up-to-date review of the current hypotheses. We consider the interpretations that can reconcile ‘Oumuamua's observed non-Keplerian trajectory with the nondetection of traditional cometary volatiles. We examine the ability of these proposed formation pathways to populate the galaxy with sufficient interstellar objects such that the detection of ‘Oumuamua by Pan-STARRS would be statistically favored. We consider two exotic ices, hydrogen and nitrogen, showing that the frigid temperature requirement for the former and the necessary formation efficiency of the latter pose serious difficulties for these interpretations. Via order-of-magnitude arguments and hydrodynamical cratering simulations, we show that impacts on extrasolar Kuiper Belt analogues are not expected to generate N2 ice fragments as large as ‘Oumuamua. In addition, we discuss observational tests to confirm the presence of these ices in future interstellar objects. Next, we examine the explanations that attribute ‘Oumuamua's properties to other compositions: ultraporous dust aggregates and thin membranes powered by solar radiation pressure, among others. While none of these hypotheses are perfectly satisfactory, we make predictions that will be testable by the Vera Rubin Observatory to resolve the tension introduced by ‘Oumuamua.
A New Insight on Balanced Augmented Lagrangian Method∗

Motivated by the recent work [He-Yuan, Balanced Augmented Lagrangian Method for Convex Programming, arXiv: 2108.08554v1, 19 Aug 2021], a novel Augmented Lagrangian Method (ALM) has been proposed for solving a family of convex optimization problem subject to equality or inequality constraint. This new method is also extended to solve the multi-block convex optimization problem. Preliminary and some new convergence results are established with the aid of variational analysis for the saddle point of the problem and the first-order optimality conditions of involved subproblems.
Estimating the Effective Lifetime of the z ∼ 6 Quasar Population from the Composite Proximity Zone Profile

The lifetime of quasars can be estimated by means of their proximity zone sizes, which are regions of enhanced flux blueward of the Lyα emission line observed in the rest-frame UV spectra of high-redshift quasars because the intergalactic gas has a finite response time to the quasars’ radiation. We estimate the effective lifetime of the high-redshift quasar population from the composite transmitted flux profile within the proximity zone region of a sample of 15 quasars at 5.8 ≤ z ≤ 6.6 with precise systemic redshifts, and similar luminosities, i.e., −27.6 ≤ M 1450 ≤ −26.4, and thus a similar instantaneous ionizing power. We develop a Bayesian method to infer the effective lifetime from the composite spectrum, including robust estimates of various sources of uncertainty on the spectrum. We estimate an effective lifetime of the quasar population as a whole of log10(tQ/yr)=5.7−0.3(−0.5)+0.5(+0.8) given by the median and 68th (95th) percentile of the posterior probability distribution. While our result is consistent with previous quasar lifetime studies, it poses significant challenges on the current model for the growth of supermassive black holes located in the center of the quasars’ host galaxies, which requires that quasar lifetimes are more than an order of magnitude longer.
Pump-induced terahertz anisotropy in bilayer graphene

We investigate the intraband nonlinear dynamics in doped bilayer graphene in the presence of strong, linearly-polarized, in-plane terahertz fields. We perform degenerate pump-probe experiments with 3.4 THz fields on doped bilayer graphene at low temperature (12 K) and find that when the pump is co-polarized with the probe beam, the differential pump-probe signal is almost double that found in the cross-polarized case. We show that the origin of this pump-induced anisotropy is the difference in the average electron effective mass in the probe direction when carriers are displaced in k-space by the pump either parallel or perpendicular to the direction of the probe polarization. We model the system using both a simple semiclassical model and a Boltzmann equation simulation of the electron dynamics with phenomenological scattering and find good qualitative agreement with experimental results.
Large spatiotemporal variability in metabolic regimes for an urban stream draining four wastewater treatment plants with implications for dissolved oxygen monitoring

Urbanization and subsequent expansion of wastewater treatment plant (WWTP) capacity has the potential to alter stream metabolic regimes, but the magnitude of this change remains unknown. Indeed, our understanding of downstream WWTP effects on stream metabolism is spatially and temporally limited, and monitoring designs with upstream-downstream comparison sites are rare. Despite this, and despite observed spatiotemporal variability in stream metabolic regimes, regulators typically use snapshot monitoring to assess ecosystem function in receiving streams, potentially leading to biased conclusions about stream health. To address these important practical issues, we assessed the spatiotemporal variability in stream metabolism at nine sites upstream and downstream of four WWTPs in a suburban stream. We used one year (2017–2018) of high-frequency dissolved oxygen (DO) data to model daily gross primary productivity (GPP) and ecosystem respiration (ER). We found that GPP was 1.7–4.0 times higher and ER was 1.2–7.2 times higher downstream of the WWTPs, especially in spring when light was not limited by canopy shading. Critically, we observed that these effects were spatially limited to the kilometer or so just downstream of the plant. These effects were also temporally limited, and metabolic rates upstream of WWTPs were not different from sites downstream of the plant after leaf-out at some sites. Across sites, regardless of their relation to WWTPs, GPP was positively correlated with potential incident light suggesting that light is the dominant control on GPP in this system. Temporal windowing of DO to proposed regulatory monitoring lengths revealed that the violation frequency of water quality criteria depended on both the monitoring interval and start date. We conclude that spatiotemporal variability in metabolism and DO are crucial considerations when developing monitoring programs to assess ecosystem function, and that evidence of WWTP effects may only arise during high light conditions and at limited scales.
Accelerating composite dark matter discovery with nuclear recoils and the Migdal effect

Large composite dark matter states source a scalar binding field that, when coupled to Standard Model nucleons, provides a potential under which nuclei recoil and accelerate to energies capable of ionization, radiation, and thermonuclear reactions. We show that these dynamics are detectable for nucleon couplings as small as $g_n \sim 10^{-17}$ at dark matter experiments, where the greatest sensitivity is attained by considering the Migdal effect. We also explore Type-Ia supernovae and planetary heating as possible means to discover this type of dark matter.
Understanding the relationship between high-commitment HRM systems, HRM system strength, work engagement and burnout

This study examined whether high-commitment HRM systems lead to a decrease in burnout and to what extent this relationship is mediated by work engagement. Furthermore, the direct relationships between high-commitment HRM systems, work engagement and burnout are examined. In addition, this study examined whether the relationship between high-commitment HRM systems and work engagement is strengthened by HRM system strength. Data were gathered by means of survey research and the sample consisted of 126 participants from eight different organizations in the Netherlands. Results indicated that a high-commitment HRM system in place will lead to higher levels of work engagement and, as a consequence, to a decrease in burnout. Unfortunately, no significant results were found for the moderating role of HRM system strength regarding the relationship between highcommitment HRM systems and work engagement. Since evidence has been found for the mediating role of work engagement, organizations should invest in high-commitment HRM systems to enhance the level of work engagement and attenuate the level of burnout. However, future research needs to further zoom in on other possible antecedents that might reduce burnout.
The Neuroprotective Effects of Moderate and Regular Caffeine Consumption in Alzheimer's Disease

The increasing numbers of elderly Alzheimer's disease (AD) patients because of a steady increase in the average lifespan and aging society attract great scientific concerns, while there were fewer effective treatments on AD progression due to unclear exact causes and pathogenesis of AD. Moderate (200-500 mg/d) and regular caffeine consumption from coffee and tea are considered to alleviate the risk of AD and have therapeutic potential. This paper reviewed epidemiological studies about the relationship of caffeine intake from coffee or/and tea with the risk of AD and summarized the caffeine-related AD therapies based on experimental models. And further well-designed and well-conducted studies are suggested to investigate the optimal dosages, frequencies, and durations of caffeine consumption to slow down AD progression and treat AD.
Mothers’ Acceptance and Willingness to Pay an Out-of-Pocket Payment for Maternal and Child Nutritional Services in Northwest Ethiopia

Background Around $3.5 trillion globally and up to $2.78 billion in Ethiopia, losses are reported annually due to malnutrition. Even if in Ethiopia nutrition services are delivered free of charge, specific public health programs may start to charge users of services that include maternal and child health services. Therefore, this study was aimed to assess mothers’ willingness to pay an out-of-pocket payment for maternal and child nutritional services and associated factors in Northwest Ethiopia. Methods A community-based cross-sectional study design was conducted at Yilmana Densa district from March to May 2017. The sample size was 569, and a systematic random sampling technique was used. Bi-variate, multivariable logistic regression, and Tobit econometric analysis models were done. Adjusted odds ratio (AOR) and B-coefficient with 95% confidence interval (CI) and p-value were used. Results A total of 545 (response rate of 95.8%) study participants were included in the study. The finding indicates that 88.8% of households were willing to pay for maternal and child nutrition services. The average amount of money to pay per visit was 19.6 ETB (CI = 18.8, 20.4) (0.84 USD). Mothers with good knowledge, good perceived family health status, medium and high perceived quality of services, and satisfaction with maternal and child nutritional services were significant variables to accept an out-of-pocket payment. In addition, the Tobit econometric model analysis reveals that knowledge, mothers’ age, pregnancy status, mothers’ educational status, wealth status, and self-reported satisfaction were identified as significant factors. Conclusion The majority of mothers were willing to accept out-of-pocket payments for maternal and child nutritional services with an average of 19.6 ETB. Therefore, strengthening awareness and improving the quality of nutritional services and maternal satisfaction may increase mothers’ willingness to pay an out-of-pocket payment.
Does Board Interlock Control High-Tech Performance? Evidence From ASEAN's Growth Countries

The aim of this study is to explain the board interlock association on high-tech firm performance in three growth ASEAN countries. The research samples were 109 high-tech listed manufacturing firms, consisting of 38 Indonesian firms, 37 Malaysian firms, and 34 Thai firms in the 2015-2018 period. The study used panel regression analysis. The research found that corporate governance through interlock directorates is a determinant of high-tech firm performance. The low human capital with high-tech capabilities in ASEAN countries can be overcome by interlocking and facilitating companies to access strategic information. This study also found a positive association between director woman interlock and corporate performance. On the other hand, an independent director who does interlocking actually has a negative effect on company performance. This research is the first research on board interlock in high-tech firms in ASEAN growth countries, Indonesia, Malaysia, and Thailand. There is still limited research involving more than one country, especially developing countries, in studying the interlock board relationship on the company performance. The highlight of this study is to compare the effects of the three interlocks, interlocking directorate, woman interlock, and independent interlock, and their associations on the profitability of high-tech firms. Investors can have an in-depth understanding of the role of the type of interlock that affects the profitability and make the right decision in investing
Avaliação da capacidade tampão salivar em biofilmes dentais in vivo expostos a bebidas ácidas Evaluation of salivary buffer capacity in dental biofilms in vivo exposed to acidic drinks

Objective: to evaluate the cariogenic and erosive potential in vivo of different acidic drinks. Method: the pH of the dental biofilm was measured by a micro-electrode type BEETRODE® (WPI Inc., England) coupled to a potentiometer (Orion 230 A). The measu-rement moments were in the "baseline", 5, 10, 15, 20, 25 and 30 minutes. The data were analyzed and the level of significance was 5%. Results: all beverages analyzed have a minimum pH below 5.5, characterizing their cariogenic potential, 75% have a minimum pH below 4.5, also confirming their erosive potential. Conclusion: all acid drinks analyzed have cariogenic and erosive potential. Soft drinks cause a more pronounced drop in pH immediately, except in the absence of sucrose, where the drop in pH is less prolonged. The industrialized liquid grape juice was the one that came closest to water, being the product of the group less cariogenic and erosive.
Employing optical flow on convolutional recurrent structures for deepfake detection

Deepfakes, or artificially generated audiovisual renderings, can be used to defame a public figure or influence public opinion. With the recent discovery of generative adversarial networks, an attacker using a normal desktop computer fitted with an offthe-shelf graphics processing unit can make renditions realistic enough to easily fool a human observer. Detecting deepfakes is thus becoming vital for reporters, social networks, and the general public. Preliminary research introduced simple, yet surprisingly efficient digital forensic methods for visual deepfake detection. These methods combined convolutional latent representations with bidirectional recurrent structures and entropy-based cost functions. The latent representations for the video are carefully chosen to extract semantically rich information from the recordings. By feeding these into a recurrent framework, we were able to sequentially detect both spatial and temporal signatures of deepfake renditions. The entropy-based cost functions work well in isolation as well as in context with traditional cost functions. However, re-enactment based forgery is getting harder to detect with newer generation techniques ameliorating on temporal ironing and background stability. As these generative models involve the use of a learnable flow mapping network from the driving video to the target face, we hypothesized that the inclusion of edge maps in addition to dense flow maps near the facial region provides the model with finer details to make an informed classification. Methods were demonstrated on the FaceForensics++, Celeb-DF, and DFDC-mini (custom-made) video datasets, achieving new benchmarks Akash Chintha’s Masters Thesis Document 9 in all categories. We also perform extensive studies to evaluate on adversaries and demonstrate generalization to new domains, consequently gaining further insight into the effectiveness of the new architectures. Akash Chintha’s Masters Thesis Document 10