Miracle-dz/UniFAR-FTUs huggingface dataset:
Economic and Health Predictors of National Postpartum Depression Prevalence: A Systematic Review, Meta-analysis, and Meta-Regression of 291 Studies from 56 Countries

Background Postpartum depression (PPD) poses a major global public health challenge. PPD is the most common complication associated with childbirth and exerts harmful effects on children. Although hundreds of PPD studies have been published, we lack accurate global or national PPD prevalence estimates and have no clear account of why PPD appears to vary so dramatically between nations. Accordingly, we conducted a meta-analysis to estimate the global and national prevalence of PPD and a meta-regression to identify economic, health, social, or policy factors associated with national PPD prevalence. Methods We conducted a systematic review of all papers reporting PPD prevalence using the Edinburgh Postnatal Depression Scale. PPD prevalence and methods were extracted from each study. Random effects meta-analysis was used to estimate global and national PPD prevalence. To test for country level predictors, we drew on data from UNICEF, WHO, and the World Bank. Random effects meta-regression was used to test national predictors of PPD prevalence. Findings 291 studies of 296284 women from 56 countries were identified. The global pooled prevalence of PPD was 17.7% (95% confidence interval: 16.6–18.8%), with significant heterogeneity across nations (Q = 16,823, p = 0.000, I2 = 98%), ranging from 3% (2–5%) in Singapore to 38% (35–41%) in Chile. Nations with significantly higher rates of income inequality (R2 = 41%), maternal mortality (R2 = 19%), infant mortality (R2 = 16%), or women of childbearing age working ≥40 h a week (R2 = 31%) have higher rates of PPD. Together, these factors explain 73% of the national variation in PPD prevalence. Interpretation The global prevalence of PPD is greater than previously thought and varies dramatically by nation. Disparities in wealth inequality and maternal-child-health factors explain much of the national variation in PPD prevalence.
An intelligibility metric based on a simple model of speech communication

Instrumental measures of speech intelligibility typically produce an index between 0 and 1 that is monotonically related to listening test scores. As such, these measures are dimensionless and do not represent physical quantities. In this paper, we propose a new instrumental intelligibility metric that describes speech intelligibility using bits per second. The proposed metric builds upon an existing intelligibility metric that was motivated by information theory. Our main contribution is that we use a statistical model of speech communication that accounts for noise inherent in the speech production process. Experiments show that the proposed metric performs at least as well as existing state-of-the-art intelligibility metrics.
First-principles investigation of electron-phonon interactions in novel superconductors

Despite over 100 years of scientific research, a full understanding of superconductivity remains elusive. While it is known that the electron-phonon interaction is responsible for the formation of Cooper pairs in conventional superconductors, many superconductors exhibit behaviour suggestive of more exotic pairing mechanisms. In this thesis, two novel superconducting materials are considered, monolayer transition metal dichalcogenide, MoS2, and iron-based superconductor, LaFeAsO1−xFx. The former is ideal for the study of the electron-phonon interaction, as it not only has potential applications as an atomically thin transistor, but also displays a dome-shaped superconductive state as a function of electron doping. In the latter, the superconductive state emerges from a magnetic parent compound upon flourine doping. Its high critical temperature is thought to be enhanced by magnetic fluctuation rather than being purely phonon-mediated. By using novel first-principles techniques, the electron-phonon interaction in electron doped single-layer MoS2 is investigated. The superconducting gap is calculated using the Migdal-Eliashberg theory, and by considering the electronic structure and lattice dynamics in this material, an explanation is provided for the experimentally observed doping-dependent critical temperature in this material. The origin of the doping-induced transition from a magnetic phase to a nonmagnetic phase in LaFeAsO1−xFx is determined. A new model to capture the effects of the fluorine dopants is developed, which has implications for the electron-phonon interaction in this material.
Real time bid optimization with smooth budget delivery in online advertising

Today, billions of display ad impressions are purchased on a daily basis through a public auction hosted by real time bidding (RTB) exchanges. A decision has to be made for advertisers to submit a bid for each selected RTB ad request in milliseconds. Restricted by the budget, the goal is to buy a set of ad impressions to reach as many targeted users as possible. A desired action (conversion), advertiser specific, includes purchasing a product, filling out a form, signing up for emails, etc. In addition, advertisers typically prefer to spend their budget smoothly over the time in order to reach a wider range of audience accessible throughout a day and have a sustainable impact. However, since the conversions occur rarely and the occurrence feedback is normally delayed, it is very challenging to achieve both budget and performance goals at the same time. In this paper, we present an online approach to the smooth budget delivery while optimizing for the conversion performance. Our algorithm tries to select high quality impressions and adjust the bid price based on the prior performance distribution in an adaptive manner by distributing the budget optimally across time. Our experimental results from real advertising campaigns demonstrate the effectiveness of our proposed approach.
Prevalence of Cerebral Atherosclerosis among Patients with Metabolic Syndrome: A Case Control Study on Egyptian Subjects

Background: The metabolic syndrome (MetS) is a clustering of vascular risk factors that tend to increase the risk of occurrence of diabetes mellitus type 2 (DMT2), cardiovascular and cerebro-vascular ischemic events. The aim of the present study is to investigate the association of extra-cranial and intracranial arterial atherosclerosis as well as silent brain infarction (SBI) with MetS, thereby determining the potential cerebrovascular atherosclerotic risk of MetS in the Egyptian population. Methods: A case control study was conducted on 50 Egyptian subjects with MetS and 30 without with age range from 40 - 60 years old. All participants were free from cerebrovascular ischemic events [stroke or transient ischemic attack (TIA)]. All participants underwent complete neurological examination, assessment of the diagnostic criteria for MetS, carotid and transcranial duplex ultrasonography (U/S) and brain MRI. Results: Preclinical carotid atherosclerosis (athero-sclerotic plaques with <50% stenosis) was associated with MetS (P value = 0.02) that persisted after adjustment for age and other confounders. There was no significant association between increased intima media thickness (IMT) and MetS. There was non-significant association between MetS and intracranial atherosclerotic disease (ICAD) or the degree of intracranial stenosis (ICS). Conversely, there was a highly significant association between MetS and SBI even after adjustment for age and other confounders (P value = 0.001). Conclusion: Metabolic syndrome is an important factor associated with mild to moderate atherosclerosis (<50% stenosis) and silent brain infarcts among asymptomatic individuals. Interventions to reduce MetS are important for prevention of subclinical and clinical cerebral atherosclerotic disease.
Exploring the Innovation in Agriculture of Developing Country ( A case Study of Nepalese Cardamom ) MASTER THESIS

Most of the developed countries achieving their economic growth by exploring the innovation in the various sector. This research paper is focused on exploring innovation in the agriculture sector of Nepal. The NSI framework is analysed to enhance the quality of large cardamom. Furthermore, qualitative research method is conducted where data is collected from the primary and secondary sources. Moreover, the findings of the research study show that Nepal has the huge possibilities to improve the economy of the country through exporting the large cardamom in to international market. Similarly, it helps to boost the living standard of Nepalese farmer. However, the quality of large cardamom should increase up to the market standard. Therefore, the requirement of improved technology, advance skills and knowledge, and research and development are highly needed in large cardamom sector. Additionally, regularity in the function of governmental and public institution, farmer friendly policy formulation, development of basic infrastructure, establishment of ICT, universities-farmer collaboration, and overall practice of good governance is highly recommended to promote innovation in Nepalese cardamom sector.
On the Prediction of Flickr Image Popularity by Analyzing Heterogeneous Social Sensory Data

The increase in the popularity of social media has shattered the gap between the physical and virtual worlds. The content generated by people or social sensors on social media provides information about users and their living surroundings, which allows us to access a user’s preferences, opinions, and interactions. This provides an opportunity for us to understand human behavior and enhance the services provided for both the real and virtual worlds. In this paper, we will focus on the popularity prediction of social images on Flickr, a popular social photo-sharing site, and promote the research on utilizing social sensory data in the context of assisting people to improve their life on the Web. Social data are different from the data collected from physical sensors; in the fact that they exhibit special characteristics that pose new challenges. In addition to their huge quantity, social data are noisy, unstructured, and heterogeneous. Moreover, they involve human semantics and contextual data that require analysis and interpretation based on human behavior. Accordingly, we address the problem of popularity prediction for an image by exploiting three main factors that are important for making an image popular. In particular, we investigate the impact of the image’s visual content, where the semantic and sentiment information extracted from the image show an impact on its popularity, as well as the textual information associated with the image, which has a fundamental role in boosting the visibility of the image in the keyword search results. Additionally, we explore social context, such as an image owner’s popularity and how it positively influences the image popularity. With a comprehensive study on the effect of the three aspects, we further propose to jointly consider the heterogeneous social sensory data. Experimental results obtained from real-world data demonstrate that the three factors utilized complement each other in obtaining promising results in the prediction of image popularity on social photo-sharing site.
Scalable application layer multicast

We describe a new scalable application-layer multicast protocol, specifically designed for low-bandwidth, data streaming applications with large receiver sets. Our scheme is based upon a hierarchical clustering of the application-layer multicast peers and can support a number of different data delivery trees with desirable properties.We present extensive simulations of both our protocol and the Narada application-layer multicast protocol over Internet-like topologies. Our results show that for groups of size 32 or more, our protocol has lower link stress (by about 25%), improved or similar end-to-end latencies and similar failure recovery properties. More importantly, it is able to achieve these results by using orders of magnitude lower control traffic.Finally, we present results from our wide-area testbed in which we experimented with 32-100 member groups distributed over 8 different sites. In our experiments, average group members established and maintained low-latency paths and incurred a maximum packet loss rate of less than 1% as members randomly joined and left the multicast group. The average control overhead during our experiments was less than 1 Kbps for groups of size 100.
Hill climbing algorithms for content-based retrieval of similar configurations

The retrieval of stored images matching an input configuration is an important form of content-based retrieval. Exhaustive processing (i.e., retrieval of the best solutions) of configuration similarity queries is, in general, exponential and fast search for sub-optimal solutions is the only way to deal with the vast (and ever increasing) amounts of multimedia information in several real-time applications. In this paper we discuss the utilization of hill climbing heuristics that can provide very good results within limited processing time. We propose several heuristics, which differ on the way that they search through the solution space, and identify the best ones depending on the query and image characteristics. Finally we develop new algorithms that take advantage of the specific structure of the problem to improve performance.
Damage Tolerant Repair Techniques for Pressurized Aircraft Fuselages

Abstract : Concerns over the safety of the continued use of aging transport aircraft have been voiced in the industry. A key component in the structural integrity of aging aircraft is the damage tolerance of fuselage structural repairs. The investigation focuses on the analysis and testing of contemporary repair methods and develops two improvements. The first technique, known as soft patching is appropriate for damage tolerant riveted repairs to incidental fuselage damage. Soft patching involves the use of high strength, moderate elastic modulus GLARE 3 fiber metal laminate patches. They extend the fatigue life of riveted repairs to monolithic aluminum fuselages while reducing life cycle costs. The second technique involves bonded crack patching of intact fatigue cracks in fuselage skins. An easy to use analysis program is presented along with analytical and test results of a low cost, high performance patch material known as GLARE 2. The findings demonstrate that GLARE 2 can replace the expensive boron/epoxy composites used in fuselage crack patching applications. Repair, Fatigue, Damage tolerance, Fuselage, Structures, Crack patching, Adhesive bonding, Surface pretreatment, Riveting, Advanced composite materials, Fiber metal laminates, GLARE, coefficient of thermal expansion.
The persona effect: affective impact of animated pedagogical agents

Animated pedagogical agents that inhabit interactive learning environments can exhibit strikingly lifelike behaviors. In addition to providing problem-solving advice in response to students’ activities in the learning environment, these agents may also be able to play a powerful motivational role. To design the most effective agent-based learning environment software, it is essential to understand how students perceive an animated pedagogical agent with regard to affective dimensions such as encouragement, utility, credibility, and clarity. This paper describes a study of the affective impact of animated pedagogical agents on students’ learning experiences. One hundred middle school students interacted with animated pedagogical agents to assess their perception of agents’ affective characteristics. The study revealed the persona eflecr, which is that the presence of a lifelike character in an interactive learning environment~ven one that is not expressive— can have a strong positive effect on student’s perception of their learning experience. The study also demonstrates the interesting effect of multiple types of explanatory behaviors on both affective perception and learning performance.
Abductive reasoning as a basis to reproduce expert criteria in ECG atrial fibrillation identification

Objective: This work aims at providing a new method for the automatic detection of atrial fibrillation, other arrhythmia and noise on short single-lead ECG signals, emphasizing the importance of the interpretability of the classification results. Approach: A morphological and rhythm description of the cardiac behavior is obtained by a knowledge-based interpretation of the signal using the Construe abductive framework. Then, a set of meaningful features are extracted for each individual heartbeat and as a summary of the full record. The feature distributions can be used to elucidate the expert criteria underlying the labeling of the 2017 PhysioNet/CinC Challenge dataset, enabling a manual partial relabeling to improve the consistency of the training set. Finally, a tree gradient boosting model and a recurrent neural network are combined using the stacking technique to provide an answer on the basis of the feature values. Main results: The proposal was independently validated against the hidden dataset of the Challenge, achieving a combined F1 score of 0.83 and tying for the first place in the official stage of the Challenge. This result was even improved in the follow-up stage to 0.85 with a significant simplification of the model, attaining the highest score so far reported on the hidden dataset. Significance: The obtained results demonstrate the potential of Construe to provide robust and valuable descriptions of temporal data, even with the presence of significant amounts of noise. Furthermore, the importance of consistent classification criteria in manually labeled training datasets is emphasized, and the fundamental advantages of knowledge-based approaches to formalize and validate those criteria are discussed.
Effects of the Oxide Coating Thickness on the Small Flaw Sizing Using an Ultrasonic Test Technique

When magnesium alloys are tested using ultrasonic techniques, their oxide coating will affect the transmitted ultrasonic energy and the accuracy of the flaw sizing. In this work, the effects of the coating thickness on the flaw sizing are investigated. An ultrasonic measurement model is employed to predict the flaw response signal, and the reflected and transmitted ultrasonic energy in the coated surface are corrected by modifying the reflection and transmission coefficients related to the effects of the coating thickness. The effectiveness of these coefficients and the ultrasonic measurement model are verified through experiments. With the correction of the coating thickness effects, the flaw-sizing curves predicted using the ultrasonic measurement model are provided. The flaws in magnesium alloy specimens with different coating thicknesses are tested, and the determined flaw sizes from these curves agree well with the actual sizes, which reveals the effectiveness of the proposed work. This work provides an effective tool to improve the flaw sizing performance using ultrasonic techniques in practical applications.
Modelling the elastic response of a polyurethane open cell foam based on a minimal surface energy approach

Within the present study the elastic response of a flexible open cell polyurethane foam was studied by means of experimental compression test and finite element (FE) modelling. The compression tests revealed a pronounced sample size effect which was taken into account using an analytical model. In order to eliminate the sample size and damage effects, a minimal sample size of at least 50 times the cell size was necessary in the case of the flexible foam. Surface evolver software was used to model the open cell foam structures. The FE unit cells are based on the well-known Kelvin cell and the more complex Weaire-Phelan cell topology. In both cases the cross sectional shape of the cell edges was completely determined by the minimization of the surface energy. The thus build FE-models possess a good resemblance to real open cell foam structures. The influence of relative density and shape anisotropy on the elastic properties of the cellular structures was analysed using the FE-models.
Multiple paths test data generation based on particle swarm optimisation

Considering path coverage as the test adequacy criterion, a modified multiple paths test data generator based on particle swarm optimisation (MMPPSO) algorithm is proposed. During the particle swarm optimisation process, each particle tracks the individual best position and the global best position. For the multiple paths coverage problem, different fitness functions are applied to assess the individual best position and the global best position in MMPPSO. The weighted summation of those branch distance functions is designed as the single path fitness function. The fitness function for the individual best position is the minimum of those single path fitness functions, which guides particles converge to a specific path. The fitness function for the global best position is the summation of those single path fitness functions, which guides the population achieve multiple paths coverage and avoid the premature convergence. The experiments implemented on some benchmarks show that the authors’ approach is more effective and more efficient than other methods, especially for complicated programs and large target path sets.
Recovering Genomics Clusters of Secondary Metabolites from Lakes Using Genome-Resolved Metagenomics

Metagenomic approaches became increasingly popular in the past decades due to decreasing costs of DNA sequencing and bioinformatics development. So far, however, the recovery of long genes coding for secondary metabolites still represents a big challenge. Often, the quality of metagenome assemblies is poor, especially in environments with a high microbial diversity where sequence coverage is low and complexity of natural communities high. Recently, new and improved algorithms for binning environmental reads and contigs have been developed to overcome such limitations. Some of these algorithms use a similarity detection approach to classify the obtained reads into taxonomical units and to assemble draft genomes. This approach, however, is quite limited since it can classify exclusively sequences similar to those available (and well classified) in the databases. In this work, we used draft genomes from Lake Stechlin, north-eastern Germany, recovered by MetaBat, an efficient binning tool that integrates empirical probabilistic distances of genome abundance, and tetranucleotide frequency for accurate metagenome binning. These genomes were screened for secondary metabolism genes, such as polyketide synthases (PKS) and non-ribosomal peptide synthases (NRPS), using the Anti-SMASH and NAPDOS workflows. With this approach we were able to identify 243 secondary metabolite clusters from 121 genomes recovered from our lake samples. A total of 18 NRPS, 19 PKS, and 3 hybrid PKS/NRPS clusters were found. In addition, it was possible to predict the partial structure of several secondary metabolite clusters allowing for taxonomical classifications and phylogenetic inferences. Our approach revealed a high potential to recover and study secondary metabolites genes from any aquatic ecosystem.
Personalized and Private Peer-to-Peer Machine Learning

The rise of connected personal devices together with privacy concerns call for machine learning algorithms capable of leveraging the data of a large number of agents to learn personalized models under strong privacy requirements. In this paper, we introduce an efficient algorithm to address the above problem in a fully decentralized (peer-to-peer) and asynchronous fashion, with provable convergence rate. We show how to make the algorithm differentially private to protect against the disclosure of information about the personal datasets, and formally analyze the trade-off between utility and privacy. Our experiments show that our approach dramatically outperforms previous work in the non-private case, and that under privacy constraints, we can significantly improve over models learned in isolation.