Miracle-dz/UniFAR-FTUs huggingface dataset:
Identifying Multiple Propagation Sources With Motif-Based Graph Convolutional Networks for Social Networks

Identifying the sources of propagation in social networks, such as the misinformation propagation, is one of the key issues recently. Most existing studies assume the underlying propagation model is known, which is difficult to obtain in practice. Recent efforts have been devoted to detect multiple sources in real-world situations, and the social influence of neighbors in the propagation is assumed to be identical. However, this assumption will result in inaccurate results as the infection state of a node is determined by its critical neighbors. In this paper, we fill this gap by capturing social influence of neighbors with structural properties in social networks. For instance, opinions are more likely to spread via closely connected friends within small groups. Here we propose a Motif-based Graph Convolutional Networks for Source Identification (MGCNSI) framework based on the GCN-based source identification approach. Specifically, different network motifs are used to capture different types of structural properties. Then each motif extracts the critical neighbors of a particular type, and a motif-based graph convolutional layer is constructed to aggregate critical neighbors for that motif. To adapt to underlying propagation mechanisms, an attention mechanism for aggregation is designed to automatically assign higher weights to more informative motifs. The empirical results demonstrate that MGCNSI outperforms several benchmark methods on both synthetic and real-world networks. The advantage is most obvious for networks with denser node neighborhoods, where MGCNSI can select critical neighbors from the larger neighbor sets. How the motifs can capture the social influence and the underlying critical paths of propagation is also illustrated.
Rotational transitions of CH2D+ determined by high-resolution IR spectroscopy

Context. Deuterated forms of CH + are responsible for deuterium fractionation in warmer environments. Current searches for CH2D + are hampered by a lack of accurate laboratory data. Aims. We demonstrate that IR spectroscopy at very high resolution can make accurate rotational predictions. Methods. By combining a low-temperature ion trap with a narrow-bandwidth IR light source, we are able to measure vibrational transitions with high accuracy. A subsequent fit using an asymmetric rotor model allows predictions of MHz accuracy or even better. Results. We predict rotational transitions up to 1.5 THz.
Quadrupole magnet design, manufacturing and the acceptance tests for METU-defocusing beamline

Middle East Technical University Defocusing Beam Line (METU-DBL) performs the Single Event Effects (SEEs) radiation tests with protons in the range of 15 MeV to 30 MeV kinetic energy at Turkish Atomic Energy Authority Proton Accelerator Facility (TENMAK NÜKEN-PAF) for space and nuclear applications. The most critical beam transfer elements in the METU-DBL are three quadrupole magnets, which are used for defocusing the beam like optical lenses. The first two quadrupole magnets were purchased commercially, and the third quadrupole magnet was designed by the METU-DBL project team and manufactured by Sönmez Transformer Inc. in Turkey. Electronic, mechanic and magnetic capability tests of this custom-designed magnet were conducted both at TUBITAK and CERN facilities. After passing the acceptance tests, the magnet was certified by CERN and then installed to METU-DBL as a third quadrupole magnet. In this paper, we present details of the design, production and performed tests of the quadrupole magnet.
Single phase to ground arcing fault identification based on wavelet packet energy

With the increasing proportion of cable lines in the distribution network, the capacitance current in the system increases, which leads to the single phase to ground arcing fault in the distribution network. However, at present, there is less research on single phase to ground arcing fault, and the type of grounding fault is always assumed to be metallic or resistive grounding, and the arcing ground fault is not identified in time, which leads to further expansion of the fault. In this paper, the transient simulation analysis of a single phase to the ground is carried out, and a new method to distinguish arcing ground fault from metal grounding fault using wavelet packet energy is proposed, which solves the problem of fault type identification. Through a large number of simulation data analyses and practical verification, it is shown that the method is not affected by the time of fault occurrence and neutral grounding mode, and has a strong anti-interference ability and high reliability.
A distributed beamforming interference minimization algorithm based on node selection

Distributed beamforming uses nodes in a wireless sensor network to transmit signals in different phases with controllable delay, to obtain coherent output signals with a gain after superposition. However, a wireless sensor network has a large topological area and wide distribution range, and it is difficult for distributed beamforming to obtain a highly directional beam as centralized beamforming does, which will cause interference to the non-target base stations. To solve this problem, a discrete adaptive dual-population cooperative differential evolution (DPCDE) algorithm is proposed, which can effectively reduce the interference by selecting nodes suitable for participating in distributed beamforming in a wireless sensor network. Simulation results show that the proposed algorithm can optimize the node set participating in distributed beamforming to minimize the interference of the wireless sensor network to the non-target base stations, and the effect is better than other classic intelligent optimization algorithms.
Short-term PV Power Prediction Model Based on VMD-CNN-IBiLSTM

Because PV power’s generation is unpredictable, so timely and accurate prediction of PV power has great practical significance and research value for daily grid dispatch and power system security. The combined forecast model presented in our study is dependent on variational modal decomposition-convolutional neural network-improved bidirectional long short-term memory neural network (VMD-CNN-IBiLSTM) with PV power as the research object. The model uses VMD to decompose PV power sequences, thus reducing the non-smoothness of the sequences; CNN to extract the essential features of the information; BiLSTM with peephole connections to learn the forward and backward features of the temporal data; and genetic algorithm to enhance the model’s variables. For validation, actual data from the Australian DKASC testbed are used. According to the experimental findings, our study’s combined model is more precise than the classical BP neural network, SVM model, and LSTM model at making predictions.
On the Relativistic Effect of the Heavy Ions with One-Electron

One of the advantages of a quantum mechanical description of a bound states system like atoms, exotic atoms, or hadronic systems is determination of the mass and the energy spectrum in the ground and excited states as in excited states. Study of the energy spectrum in Coulomb potential of atomic systems, in relativistic conditions due to requirements of using higher grades of relativistic corrections have attracted physics theoreticians. The ability to create mono-electron ions of heavy, semi-heavy, strange atoms and/or hadronic atoms in laboratory conditions has boomed the need of more precise and meticulous corrections. In this article, we begin our discussion of the heavy ions with one-electron interactions in the spherical-symmetric potential (i.e. Coulomb), using quantum field theory, and quantum mechanic’s technics. These technics are the great triumphs of our technically description. Therefore, too many different models and methods explained the energy spectrum of the heavy ions. It has been determined, in the numerous energy intervals based on potential models and given corrections in the relativistic limit. This document contains a theoretical definition on the heavy ions with one-electron characteristics that describes the nature of interactions this limit. Relativistic nature of interactions directly effect on the mass of particles and it is a good reason to study how relativistic mass could effect on the energy spectrum for the n-body particles system that bounding to each other by electrostatic potential. Thus, the mass and energy spectrum of a coupled state are analytically determined. The mechanism of production of a constituent mass of particles forming a coupled state for the heavy ions with one-electron was described.
Effects of Three-Hour Wearing Personal Protective Equipment on Heart Rate Variability in Healthcare Workers for the Treatment of COVID-19 Patients

Background Personal protective equipment (PPE), an essential shield to protect healthcare workers (HCWs) during the COVID-19 pandemic, has been reported to affect their heart rate variability (HRV). Objective To investigate the changes of very short-term heart rate variability in HCWs after three hours of wearing PPE to treat COVID-19 patients at different working times and intensities, and related factors. Methods Sixty-five healthy HCWs were enrolled at the Number 2 Infectious Field Hospital (formed by Military Hospital 103), Vietnam. Two-minute 12-lead electrocardiograms were recorded before wearing and after removing PPE. Results After three hours of wearing PPE, the mean heart rate of HCWs increased (p = 0.048) meanwhile, the oxygen saturation decreased significantly (p = 0.035). Standard deviation of all normal to normal intervals (SDNN), mean intervals RR (mean NN), and root mean square successive difference (rMSSD) after wearing PPE was also reduced significantly. SDNN, Mean NN, and rMSSD decreased as the working intensity increased (as in mild, moderate, and severe patient departments). In univariate regression analysis, logSDNN, logmean NN and logrMSSD were positively correlated with SpO2 and QT interval (r = 0.14, r = 0.31, r = 0.25; r = 0.39, r = 0.77, r = 0.73, respectively) and were negatively correlated with ambient temperature inside PPE (r = −0.41, r = −0.405, r = −0.25, respectively) while logmean NN and log rMSSD were negatively correlated with diastolic blood pressure (r = −0.43, r = −0.39, respectively). In multivariable regression analysis, logSDNN and logmean NN were negatively correlated to ambient temperature inside PPE (r = −0.34, r = −0.18, respectively). Conclusion Time-domain heart rate variability decreased after wearing PPE. Time-domain HRV parameters were related to ambient temperature inside PPE, diastolic blood pressure, QT interval, and SpO2.
Epidemiology of Patellar Tendinopathy in Athletes and the General Population: A Systematic Review and Meta-analysis

Background: Patellar tendinopathy (PT) mainly affects athletes who use the tendon for repeated energy storage and release activities. It can have a striking impact on athletes’ careers, although data on its real prevalence and incidence are sparse. Research efforts should start from the results of reliable and updated epidemiological research to help better understand the impact of PT and underpin preventative measures. Purpose: To determine the prevalence and incidence of PT in athletes and the general population. Design: Systematic review; Level of evidence, 3. Methods: A systematic review of the literature was performed on January 17, 2022, and conducted according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The PubMed, Web of Science, and Wiley Cochrane Library databases were searched for epidemiological reports of any evidence level and clinical studies reporting data on the incidence or prevalence of PT for the 11,488 retrieved records. The primary endpoint was the prevalence and incidence of PT in sport-active patients. The secondary endpoints were PT prevalence and incidence in subgroups of athletes based on sex, age, sport type, and sport level played, as well as the same epidemiological measures in the general population. Results: A total of 28 studies, with 28,171 participants, were selected and used for the qualitative and quantitative analysis. The general and athletes’ populations reported an overall PT prevalence of 0.1% and 18.3%, respectively. In athletes, the prevalence of PT was 11.2% in women and 17% in men (P = .070). The prevalence of PT in athletes <18 years was 10.1%, while it was 21.3% in athletes ≥18 years (P = .004). The prevalence of PT was 6.1% in soccer players, 20.8% in basketball players, and 24.8% in volleyball players. Heterogeneous PT diagnostic approaches were observed. Higher prevalence values were found when PT diagnoses were made using patient-reported outcomes versus clinical evaluations (P = .004). Conclusion: This review demonstrated that PT is a common problem in the male and female sport-active populations. There are twice as many athletes aged ≥18 years than there are <18 years. Volleyball and basketball players are most affected by PT.
Diverse policy maker perspectives on the mental health of pregnant and parenting adolescent girls in Kenya: Considerations for comprehensive, adolescent-centered policies and programs

The pregnancy rate in Kenya among adolescent girls is among the highest in the world. Adolescent girls experience increased risk of anxiety and depression during pregnancy and postpartum which can result in poor health outcomes for both mother and baby, and negatively influence their life course. Mental health is often given low priority in health policy planning, particularly in Sub-Saharan Africa (SSA). There is an urgent need to address the treatment gap and provide timely mental health promotion and preventative services, there is a need to focus on the shifting demographic of SSA—the young people. To understand perspectives on policymakers on the mental health prevention and promotion needs of pregnant and parenting adolescent girls, we carried out a series of interviews as part of UNICEF funded helping pregnant and parenting adolescents thrive project in Kenya. We interviewed 13 diverse health and social policy makers in Kenya to understand their perspectives on the mental health experiences of pregnant and parenting adolescent girls and their ideas for optimizing mental health promotion. Six principal themes emerged including the mental health situation for adolescent girls, risk factors for poor mental health and barriers to accessing services for adolescent girls, health seeking behavior effect on maternal and child health outcomes, mental health promotion, protective factors for good mental health, and policy level issues. Examination of existing policies is required to determine how they can fully and effectively be implemented to support the mental health of pregnant and parenting adolescent girls.
A novel framework based on deep learning for COVID-19 diagnosis from X-ray images

Background The coronavirus infection has endangered human health because of the high speed of the outbreak. A rapid and accurate diagnosis of the infection is essential to avoid further spread. Due to the cost of diagnostic kits and the availability of radiology equipment in most parts of the world, the COVID-19 detection method using X-ray images is still used in underprivileged countries. However, they are challenging due to being prone to human error, time-consuming, and demanding. The success of deep learning (DL) in automatic COVID-19 diagnosis systems has necessitated a detection system using these techniques. The most critical challenge in using deep learning techniques in diagnosing COVID-19 is accuracy because it plays an essential role in controlling the spread of the disease. Methods This article presents a new framework for detecting COVID-19 using X-ray images. The model uses a modified version of DenseNet-121 for the network layer, an image data loader to separate images in batches, a loss function to reduce the prediction error, and a weighted random sampler to balance the training phase. Finally, an optimizer changes the attributes of the neural networks. Results Extensive experiments using different types of pneumonia expresses satisfactory diagnosis performance with an accuracy of 99.81%. Conclusion This work aims to design a new deep neural network for highly accurate online recognition of medical images. The evaluation results show that the proposed framework can be considered an auxiliary device to help radiologists accurately confirm initial screening.
Monte Carlo model for the argon ions and fast argon atoms in a radio-frequency discharge

A three-dimensional Monte Carlo model has been developed for the argon ions and fast argon atoms in the RF sheath of a capacitively coupled RF glow discharge in argon. Our interest in the argon ions and fast argon atoms in the RF sheath arises from the fact that the glow discharge under study is used as sputtering source for analytical chemistry. This source operates at typical working conditions of a few torr pressure and about 10 W incoming power. The argon ion and atom Monte Carlo model has been coupled to a hybrid Monte Carlo-fluid model for electrons and argon ions developed before to obtain fully self-consistent results. Typical results of this model include, among others, the densities, fluxes, collision rates, mean energies, and energy distributions of the argon ions and atoms. Moreover, we have investigated how many RF cycles have to be followed before periodic steady state is reached, and the effects of all previous RF cycles are correctly accounted for. This is found to be the case for about 20-25 RF cycles.
Mycotoxin tolerance affects larval competitive ability in Drosophila recens (Diptera: Drosophilidae)

Abstract Certain mycophagous Drosophila species are the only known eukaryotes that can tolerate some highly potent mycotoxins. This association between mycophagy and mycotoxin tolerance is well established because Drosophila species that switch hosts from mushrooms to other food sources lose their mycotoxin tolerance trait without any evolutionary lag. These findings suggest that mycotoxin tolerance may be a costly trait to maintain. In this study, we attempted to identify whether mycotoxin tolerance has a fitness cost. Larval competitive ability is a vital fitness trait, especially in holometabolous insects, where the larvae cannot move to a new host. Furthermore, larval competitive ability is known to be associated with many critical life-history traits. Here we studied whether mycotoxin tolerance adversely affects larval competitive ability on isofemale lines from 2 distinct locations. We observed that the extent of mycotoxin tolerance affected larval competitive ability, but only in isofemale lines from one location. Additionally, we observed that the high mycotoxin-tolerant isofemale lines from the same location showed poor survival to eclosion. This study shows that mycotoxin tolerance is associated with fitness costs and provides preliminary evidence of an association between local adaptation and mycotoxin tolerance.
Facile access to 3-sulfonylquinolines via Knoevenagel condensation/aza-Wittig reaction cascade involving ortho-azidobenzaldehydes and β-ketosulfonamides and sulfones

Quinoline-based sulfonyl derivatives, and especially sulfonamides, are relevant and promising structures for drug design. We have developed a new convenient protocol for the synthesis of 3-sulfonyl-substituted quinolines (sulfonamides and sulfones). The approach is based on a Knoevenagel condensation/aza-Wittig reaction cascade involving o-azidobenzaldehydes and ketosulfonamides or ketosulfones as key building blocks. The protocol is appropriate for both ketosulfonyl reagents and α-sulfonyl-substituted alkyl acetates providing the target quinoline derivatives in good to excellent yields.
Two new genera and one new species of the tribe Adeshini (Hymenoptera, Braconidae, Braconinae) from India and South Africa

﻿Abstract Two new genera and one new species of the Braconinae tribe Adeshini are described and illustrated: Crenuladesha Ranjith & Quicke, gen. nov., type species Adeshanarendrani Ranjith, 2017, comb. nov. from India, and Protadesha Quicke & Butcher, gen. nov., type species Protadeshaintermedia Quicke & Butcher, sp. nov. from South Africa. The former lacks the mid-longitudinal propodeal carina characteristic of the tribe, and the latter displays less derived fore wing venation with two distinct abscissae of vein 2CU. A molecular phylogenetic analysis is included to confirm their correct placement. Since neither of the two new genera displays all of the characters given in the original diagnosis of the Adeshini a revised diagnosis is provided, as well as an illustrated key to the genera.
Selective detection of tricyclazole by optical technique using thiomalic acid–modified Au and Ag nanoparticle mixtures

This study proposes the use of thiomalic acid–modified Au and Ag nanoparticle mixtures (TMA-Au/AgNP mixes) for the selective detection of tricyclazole. Upon the addition of tricyclazole, the color of TMA-Au/AgNP mixes solution changes from orange-red to lavender (red-shift). According to the density-functional theory calculations, tricyclazole-induced aggregation of TMA-Au/AgNP mixes through electron donor–acceptor interactions was proved. The sensitivity and selectivity of the proposed method are affected by the amount of TMA, volume ratio of TMA-AuNPs to TMA-AgNPs, pH value, and buffer concentration. The ratio of absorbance (A654/A520) of TMA-Au/AgNP mixes solution is proportional to the concentration of tricyclazole over the range 0.1–0.5 ppm with a linear correlation (R2 = 0.948). Moreover, the limit of detection was estimated at 0.028 ppm. The practicality of TMA-Au/AgNP mixes was validated for the determination of tricyclazole concentration in real samples (spiked recovery was 97.5%–105.2%), demonstrating its advantages of simplicity, selectivity, and sensitivity.
An ultrasound-guided no incision banding method for the treatment of arteriovenous fistula high-flow in hemodialysis

Abstract Objective High-flow vascular access is one of the serious complications in the maturation and subsequent use of arteriovenous fistula (AVF). We adopted a novel surgical approach named no incision limited ligation indwelling needle assisted- revision (NILLINR) to treat high-flow of the hemodialysis vascular access and ascertained the outcomes by regular follow-up visits. Methods This is a retrospective study. 26 hemodialysis patients with symptomatic high-flow access (access flow > 1500 mL/min) were treated with the novel banding method without incision between June 2018 and October 2020. The flow of the brachial artery before and after the restriction was measured by experienced clinicians by using the duplex Doppler ultrasound (DUS). All 26 patients were followed up for up to 1 year. Meanwhile, the brachial artery flow was recorded at 6 months and 1 year after restriction. Results Of all 26 patients included in this study, the mean access flow volume decreased from 2196.2 ± 416.9 mL/min (mean ± SD) to 679.2 ± 67.1 mL/min immediately after the operation. During the follow-up, the volume flow of the brachial artery was still within the restricted range at 6 months (mean ± SD, 720.2 ± 164.7 mL/min) and 1 year (mean ± SD, 713.9 ± 173.8 mL/min) after the operation. Meanwhile, the mean duration of the operation is 8.5 ± 3.3 min, and there is no bleeding or rupture. Conclusion This novel no-incision limited ligation indwelling needle-assisted revision is a safe, effective, and time-saving option to treat high-flow access.