Miracle-dz/UniFAR-FTUs huggingface dataset:
University of Groningen Fathers with PTSD and depression in pregnancies complicated by preterm preeclampsia or PPROM Stramrood,

Purpose To assess prevalence and risk factors for posttraumatic stress disorder (PTSD) and depression in fathers after early preeclampsia (PE) or preterm premature rupture of membranes (PPROM). Methods Partners of patients hospitalized for PE or PPROM and partners of healthy controls completed PTSD (PSS-SR) and depression (BDI-II) questionnaires during pregnancy (t1) and 6 weeks postpartum (t2). 85 of the 187 eligible men participated (51 partners of patients, 34 partners of control) at t1, and 66 men participated both time points. Results No significant differences were found between partners of patients and partners of controls in symptoms of PTSD and depression (t1: p = 0.28 for PTSD and p = 0.34 for depression; t2: p = 0.08 for PTSD and p = 0.31 for depression). For partners of patients, correlation between PTSD and depression sum-scores was 0.48 (p \ 0.001) at t1 and 0.86 (p \ 0.001) at t2. Within-couple correlation was low and not significant during pregnancy, but strong at postpartum (PSS-SR: r = 0.62, p \ 0.001; BDI-II: r = 0.59, p \ 0.001). Higher paternal age was associated with more symptoms of PTSD and depression postpartum in partners of patients. Symptoms of PTSD and depression during pregnancy predicted the occurrence of PTSD symptoms following childbirth in partners of patients. Conclusions Symptoms of PTSD and depression occurred at a similar rate in partners of women with PE or PPROM and partners of healthy pregnant controls. Symptoms of PTSD and depression during pregnancy predicted the occurrence of PTSD symptoms following childbirth. Increased paternal age predicted more symptoms of PTSD and depression postpartum. At 6 weeks postpartum, a strong association was found between men and women in symptoms of PTSD and depression.
Effectiveness of Respiratory Therapy on Respiratory Parameters and Behavioural Response of children with Lower Respiratory Tract Infection

The present study investigated the effectiveness of respiratory therapy on respiratory parameters and behavioural response of children with lower respiratory tract infections. The conceptual framework for the study was based on Betty Neuman’s system model. The research design selected for this study was quasi experimental pre test post test control group research design. Seventy children with LRTI were selected by purposive sampling. Results of the study revealed that respiratory therapy had a statistically significant effect in improving respiratory parameters such as respiratory rate, oxygen saturation, breathing pattern, air entry and abnormal breath sounds at 0.05 level. There was a statistically significant difference in the behavioural response in terms of cry, activity, comfort, social interaction, facial expression and feeding of children with lower respiratory tract infection between control and experimental group at
Elastic Stack Ability Test Monitoring Slowloris Attack on Digital Ocean Server

— Servers have a central role in computer network. The server is in charge of serving user requests with various types of services. Every server activity in handling these things will generate different types of logs. Information from this large amount of logs is often ignored and has not been widely used as material for analyzing the performance of the server itself. In this study, Elastic Stack is functioned as a system that handles upstream to downstream processes starting from collection, transformation, and storage as well as graphical visualization of the Nginx web server given an attack scenario in the form of massive incoming connection requests and server login access attempts. The Elastic Stack components used as log collectors are Filebeat and Metricbeat for system metric data. For testing attacks using the Slowloris tool which will consume web server resources. The results of the research that have been carried out are when there are 500 incoming connections, the web server can serve requests normally, at 1000 connections there are some packets that are not served, the server becomes unable to access when it reaches a total of 2000 incoming connections. Metric data in the form of CPU Usage and Memory Usage are affected, although not significantly. Identification of IP Address shows the source of the attack comes from Singapore, according to the domicile of the attacker's computer. All access data in the form of username, time, origin of region trying to enter the server are recorded by the system.
Gunshot Detection in Wildlife using Deep Learning

The fight against the illegal hunting of African wildlife is a never-ending process. In order to preserve animal habitats and save them from extinction, many national parks utilize surveilling solutions to prevent, detect and locate intruders. One strategy to detect and locate the illegal hunters or so-called poachers is to detect and locate the gunshot sounds using an acoustic surveillance system consisting of embedded devices scattered within the park. The embedded devices–so-called end-nodes surveil the environment continuously, processing the sound events gathered and converted to audio by the acoustic sensors. Then, using a deep learning algorithm, any sound event classified as a gunshot is reported to the authorities. This research study proposes a deep learning model for gunshot sound recognition in African wildlife. It also investigates a potential correlation between gunshot sound recognition accuracy, signal-to-noise ratio (SNR), and shooter distance. To this end, gunshot and ambient sounds such as Savanna wildlife, rain, and thunder were collected and synthesized to simulate different scenarios. Various experiments were conducted using this data to investigate the influence of different parameters on gunshot recognition accuracy. Our analysis revealed the negative effect of the weather conditions, such as rain and thunderstorms, on the model accuracy. The obtained results also showed a positive correlation between the gunshot recognition accuracy and SNR. Since SNR is negatively correlated to the shooter distance when both noise and signal levels are constant, we have proved that the gunshot recognition accuracy also negatively correlates with the distance. Finally, a single CNN (convolutional neural network) model is proposed for gunshot sound recognition in African wildlife. The model performs acceptably in three different weather conditions. The gunshot recognition accuracy for five shooting ranges is also provided based on the uncovered correlation.
Universal Graph Convolutional Networks

Graph Convolutional Networks (GCNs), aiming to obtain the representation of a node by aggregating its neighbors, have demonstrated great power in tackling various analytics tasks on graph (network) data. The remarkable performance of GCNs typically relies on the homophily assumption of networks, while such assumption cannot always be satisﬁed, since the heterophily or randomness are also widespread in real-world. This gives rise to one fundamental question: whether networks with different structural properties should adopt different propagation mechanisms? In this paper, we ﬁrst conduct an experimental investigation. Surprisingly, we discover that there are actually segmentation rules for the propagation mechanism, i.e., 1-hop, 2-hop and k -nearest neighbor ( k NN) neighbors are more suitable as neighborhoods of network with complete homophily, complete heterophily and randomness, respectively. However, the real-world networks are complex, and may present diverse structural properties, e.g., the network dominated by homophily may contain a small amount of randomness. So can we reasonably utilize these segmentation rules to design a universal propagation mechanism independent of the network structural assumption? To tackle this challenge, we develop a new universal GCN framework, namely U-GCN. It ﬁrst introduces a multi-type convolution to extract information from 1-hop, 2-hop and k NN networks simultaneously, and then designs a discriminative aggregation to sufﬁciently fuse them aiming to given learning objectives. Extensive experiments demonstrate the superiority of U-GCN over state-of-the-arts. The code and data are available at https://github.com/jindi-tju.
Factors Determining Successful Exclusive Breastfeeding among Working Mothers in Indonesia: An Exploration by Using Positive Deviance Concept: Analysis of Secondary Data of Demographic Health Survey Indonesia 2017

Despite the fact that exclusive breastfeeding brings a lot of benefits for infants and mothers, breastfeeding rates in Indonesia remain low, at 42% of all infants aged 0-5 months in 2012. This study aims to apply the positive deviance concept to explore the factors determining the success of exclusive breastfeeding among mothers in a high-risk group in Indonesia. The study used the cross-sectional, DHS 2017 data of 1.178 mothers with 0-5 months old infants in Indonesia. Chi-square and multiple logistic regression tests with 95% confidence intervals are used in identifying the factors associated with exclusive breastfeeding. Maternal working status is shown to be the most significant risk factor in preventing mothers to breastfeed exclusively. Furthermore, the data suggest that, out of 376 working mothers, 45,5% can be identified as positive deviants – those succeeding in implementing exclusive breastfeeding. The study finds ante-natal care (ANC) visits, post-natal care (PNC) visits, and economic status displayed significant association with exclusive breastfeeding among working mothers. In the sample, ANC visit is the dominant factor associated with exclusive breastfeeding (OR: 1,831; 95% CI: 1,075-3,118). Completion of ANC and PNC visits should be taken to consideration in improving exclusive breastfeeding rate among working mothers in Indonesia.
A Novel Butler Matrix Based on Half-Mode Substrate Integrated Waveguide Technology

In this paper a novel Butler matrix based on Half-Mode Substrate Integrated Waveguide(HMSIW) technology is proposed and implemented.The prototype is fabricated at X-band(10GHz) with a standard double side PCB process.The measured insertion loss is 2dB;the size of the prototype is 24cm×6.5cm. Compared with conventional microstrip technology,this structure has a lower insertion loss and better power handling capability.Compared with that based on SIW technology,the size of the Butler matrix is considerably reduced.Thus,it is a good candidate for beam-forming networks of microwave or millimeter wave multi-beam antennas.The detailed design process and the measured results are presented.
Faking Fake News for Real Fake News Detection: Propaganda-Loaded Training Data Generation

Despite recent advances in detecting fake news generated by neural models, their results are not readily applicable to effective detection of human-written disinformation. What limits the successful transfer between them is the sizable gap between machine-generated fake news and human-authored ones, including the notable differences in terms of style and underlying intent. With this in mind, we propose a novel framework for generating training examples that are informed by the known styles and strategies of human-authored propaganda. Specifically, we perform self-critical sequence training guided by natural language inference to ensure the validity of the generated articles, while also incorporating propaganda techniques, such as appeal to authority and loaded language. In particular, we create a new training dataset, PropaNews, with 2,256 examples, which we release for future use. Our experimental results show that fake news detectors trained on PropaNews are better at detecting human-written disinformation by 3.62–7.69% F1 score on two public datasets.
Influence of vehicle-exterior image on sound quality evaluation of Japanese and German drivers

In this study, an influence of vehicle-exterior image on sound quality evaluation was investigated. Luxuryor sporty-vehicle image was presented to Japanese or German frequent drivers, while listening to an acceleration sound. Subsequently, loudness, luxury and sportiness for the sound were rated. As the result, significant influence was not observed in the loudness and luxury evaluation in both nationalities. However, sportiness rating to the presented sound was markedly increased when the sporty-vehicle image was presented with the sound. Consequently, it appeared that the influence of the vehicle-exterior image on the sound quality rating differs depending on the evaluation parameter and sportiness is markedly affected by the visual information.
Gallium phosphide nanowires for “biological concentrations” ammonia detection

Ammonia is widespread chemical compound found both in nature and in human body, and its detection is very important in biology and medicine as well as in environmental monitoring systems. This work is aimed at fabrication and study of precise, technological and relatively cheap ammonia sensors compatible with a liquid medium. Here we use GaP epitaxial nanowires (NWs) as adsorption elements capable of ammonia detection fabricated via a simple protocol. The device properties are studied in terms of change in sensor impedance spectrum upon presence of ammonia. Physical interpretation of the impedance spectra in the presence of water and ammonia is given. GaP–based device exhibit sufficient response to the ammonia presence with the detection limit lower than 2.5 ppm.
Modulation of the dielectric property of Ga2O3/TiO2 nanolaminates and their improvement on the electroluminescence from devices based on Er-doped Al2O3 nanofilms

Metal-oxide-semiconductor light-emitting devices (MOSLEDs) based on the erbium doped Al2O3 nanofilms still suffer from insufficient and unstable electrical injection, and the dielectric protection layers play an important role in their performance. Here, Ga2O3/TiO2 (GTO) nanolaminate films are fabricated by atomic layer deposition, the dielectric constant and leakage current of which are gradually regulated by alternating the component ratios. The tolerance to electric field and electron injection for the Al2O3:Er MOSLEDs is significantly improved, leading to the enhanced electroluminescence performance. The optimal GTO nanolaminate with thickness ratio of 4:6 could withstand the maximum breakdown electric field reaching 4.17 MV cm−1 while maintaining a high electric field of 8.4 ± 0.1 MV cm−1 within the luminescent Al2O3:Er layer (under injection of 0.3 A cm−2), resulting in the optical power density up to 11.37 mW cm−2. Much higher excitation efficiencies are also achieved with external quantum efficiency of 19.35%, and the operation time of the prototype Al2O3:Er MOSLEDs is significantly enhanced by more than two orders of magnitude from 48 s (I= 0.2 μA) to 7308 s (I = 1 μA). The design principle of the dielectric nanolaminates has been deduced, considering the dielectric and conductive contribution of different oxides, which supplies a promising route to further explore the application of optoelectronic devices based on oxides.
Application of refined mixed finite element model for analysis of laminated composite beams

Analysis of laminated composite members is always a critical phenomenon. To address this issue, the authors developed the six nodes two dimensional refined mixed finite element model (r-MFEM) for accurate analysis of laminated composite beam. This model has transverse stress σZ and τxz as primary variables along with displacement u and w. Equilibrium equations are used to refine the variation of primary variables in the formulation. In the present paper, various laminated composite beams have been analysed by the six nodes two-dimensional r-MFEM element to show the model’s versatility. The results obtained has been compared with the other author’s work.
A Study of the Aerodynamic Behavior of a NREL Phase VI Wind Turbine Using the CFD Methodology

Chen, Yen-Pin, M.S.Egr. , Department of Mechanical and Materials Engineering, Wright State University, 2011. A Study of the Aerodynamic Behavior of a NREL Phase VI Wind Turbine Using the CFD Methodology Wind energy is an abundant natural resource that people have been trying to tap in recent decades. More and more wind turbines are being built to solve the world’s energy shortage problem. For a wind turbine, power extracted from the wind by the rotor and the torque applied to the wind turbine blades are important issues in the design process. Thus there is a need to predict the performance of wind turbine blades using computer modeling. This work shows the results of a computational fluid dynamic simulation developed to predict the air flow field and associated aerodynamic quantities around the moving blades of a wind turbine. The commercial software package SolidWorks was used to construct the geometrical model. Two commercial CFD codes, SC/Tetra and FLUENT, were used to do the fluid simulations. This work was performed in two phases. First a two-dimensional airfoil simulation was modeled to investigate the aerodynamic coefficients Cl, Cd, and Cm for the S809 airfoil. Validation of the CFD model was also examined. The second phase of this modeling work was a three-dimensional model of the flow around the NREL (National Renewable Energy Laboratory) Phase VI wind
A Genomic Snapshot of Enterococcus faecalis within Public Hospital Environments in South Africa

Enterococci are among the most common opportunistic hospital pathogens. This study used whole-genome sequencing (WGS) and bioinformatics to determine the antibiotic resistome, genetic support, clones and phylogenetic relationship of Enterococcus faecalis isolated from hospital environments in South Africa. Isolates were recovered from 11 frequently touched sites by patients and healthcare workers in different wards at 4 levels of healthcare (A, B, C and D) in Durban, South Africa. Following microbial identification and antibiotic susceptibility tests. Of the 245 E. faecalis isolates identified, 38 were subjected to WGS on the Illumina MiSeq platform. The tet(M) (31/38, 82%) and erm(C) (16/38, 42%) genes were the most common antibiotic resistome found in isolates originating from the different hospital environments which corroborated with their antibiotic resistance phenotypes. The isolates harboured mobile genetic elements consisting of plasmids (n=11) and prophages (n=14), that were mostly clone-specific. Of note, a large number insertion sequence (IS) families were found with the IS3 (55%), IS5 (42%), IS1595 (40%) and Tn3 Transposon been the most predominate. Microbial typing using WGS data revealed 15 clones with 6 major sequence types (ST) belonging to ST16 (n =7), ST40 (n = 6), ST21 (n =5), ST126 (n = 3), ST23 (n =3) and ST386 (n=3). Phylogenomic analysis showed that the major clones were mostly conserved within specific hospital environments. However, further metadata insights revealed the complex intra-clonal spread of these E. faecalis major clones between the sampling sites within each specific hospital setting. The results of these genomic analyses will offer insights into E. faecalis in the hospital environments relevant in the design of optimal infection prevention strategies in hospital settings.
Sex differences in functional network topology over the course of aging in 37543 UK Biobank participants

Aging is a major risk factor for cardiovascular and neurodegenerative disorders, with considerable societal and economic implications. Healthy aging is accompanied by changes in connectivity between and within resting-state functional networks, however, there is no consensus on the impact of sex on these age-related alterations. Here, in a large cross-sectional sample of 37543 UK Biobank participants, we show that multilayer measures that capture the interaction between positive and negative connections provide crucial information on the impact of sex on age-related changes in network topology, being closely related to cognitive, structural, and cardiovascular risk factors that have been shown to differ between men and women. We also provide additional insights into the genetic influences on multilayer connectivity changes that occur during aging. Our findings indicate that multilayer measures contain previously unknown information on the relationship between sex and age, opening up new avenues for research into functional brain connectivity in aging.
The IgA in milk induced by SARS-CoV-2 infection is comprised of mainly secretory antibody that is neutralizing and highly durable over time

Approximately 10% of infants infected with SARS-CoV-2 will experience COVID-19 illness requiring advanced care. A potential mechanism to protect this population is passive immunization via the milk of a previously infected person. We and others have reported on the presence of SARS-CoV-2-specific antibodies in human milk. We now report the prevalence of SARS-CoV-2 IgA in the milk of 74 COVID-19-recovered participants, and find that 89% of samples are positive for Spike-specific IgA. In a subset of these samples, 95% exhibited robust IgA activity as determined by endpoint binding titer, with 50% considered high-titer. These IgA-positive samples were also positive for Spike-specific secretory antibody. Levels of IgA antibodies and secretory antibodies were shown to be strongly positively correlated. The secretory IgA response was dominant among the milk samples tested compared to the IgG response, which was present in 75% of samples and found to be of high-titer in only 13% of cases. Our IgA durability analysis using 28 paired samples, obtained 4–6 weeks and 4–10 months after infection, found that all samples exhibited persistently significant Spike-specific IgA, with 43% of donors exhibiting increasing IgA titers over time. Finally, COVID-19 and pre-pandemic control milk samples were tested for the presence of neutralizing antibodies; 6 of 8 COVID-19 samples exhibited neutralization of Spike-pseudotyped VSV (IC50 range, 2.39–89.4ug/mL) compared to 1 of 8 controls. IgA binding and neutralization capacities were found to be strongly positively correlated. These data are highly relevant to public health, not only in terms of the protective capacity of these antibodies for breastfed infants, but also for the potential use of such antibodies as a COVID-19 therapeutic, given that secretory IgA is highly in all mucosal compartments.
Breast cancer in Trinidad and Tobago: Etiopathogenesis, histopathology and receptor study

Background: Breast Carcinoma (BCa) is the leading cause of cancer among females in Trinidad and Tobago (TnT). This twin-island has a diversified population of 1.3 million individuals that display and are exposed to a variety of lifestyle choices that have been linked to the development of BCa. Therefore, this study aimed to identify the risk factors that influence the development of BCa, analyze the common histopathological details, and categorize BCa based on receptor study. Methods: Cancer information for 120 BCa cases at Eric Williams Medical Sciences Complex from 2012 to 2019 was retrieved, analyzed, and statistically estimated. The clinical details were categorized based on data tabulations, and histological assessment was performed to identify specific features. The receptor analysis was classified based on estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor-2 (HER-2neu) staining intensity. A descriptive data analysis and comparison were statistically evaluated in all these cases. Results: Epidemiological factors influencing the development of BCa were age with a peak of 56–65 years 27.5% (n = 33), ethnicity predominated in Indo-Trinidadians 48.33% (n = 58), and marital status primarily in unmarried/single/widowed patients 55% (n = 66). Infiltrating ductal carcinoma was the principal histopathological type 91.66% (n = 110). Receptor analysis revealed ER/PR + HER-2neu as the most common type 40% (n = 18) for therapeutic surveillance. Conclusion: This study highlights various epidemiological factors that influence the development of BCa among females in TnT. Histopathological analysis and receptor studies would provide a useful link between the tumor behavior and its prognosis.