Miracle-dz/UniFAR-FTUs huggingface dataset:
A multicenter clinical study on the treatment for children's acute bronchiolitis by nebulized recombinant human interferon α 1b

ObjectiveTo evaluate effectiveness and safety of treatment for children's acute bronchiolitis by nebulizedrecombinant human interferon α 1b in a multicenter clinical study,and to recommend a reasonable clinical dosage for children.MethodsWith the design of a randomized, controlled, multi-center study, totally 330 children who were hospitalized fromDecember 2012 to May 2013 for acute bronchiolitis were randomly divided into three groups. The control group had routineand symptomatic treatment. Therapy groups inhaled human recombinant interferon α1b 2 μg/kg and 4 μg/kg respectively,twice a day plus routine treatment, for 5 to 7 days. We used four-level-scoring method to evaluate disease conditions andperformed pathogen detections.Results(1)The total improvement rates of low and high dose group were 92.3% and 95.0%,respectively, which were significantly higher than controlgroup(85.3%, P0.05). High dose group had obviouslyimproved primary index(wheezing, wheezing rale and threedepression sign)compared with low dose group. The dis-appearance time of symptoms was shortened significantly(P0.05), and the improvement of three depression signswas obvious(P0.01). (2)Treatments within and after 72 hwere both effective, but the total improvement rate within 72 h was better. (3)The effectiveness of interferon α1b in RSVpositive children was significantly better than in RSV negative children. Effectiveness of treatment group was superior to that of control group. (4)All children did not show irritationsymptoms in local respiratory tracts, and no serious adverse reactions was observed.ConclusionThe treatment for acutebronchiolitis in young children by nebulized human recombinant interferon α1b is effective, which shortened the durationof symptoms, with no safety problem. The effect is better when used in the early stage.
Augmented input estimation in multiple maneuvering target tracking

This paper presents augmented input estimation (AIE) for multiple maneuvering target tracking. Multi-target tracking (MTT) is based on two main parts, data association and estimation. In data association (DA), the best observations are assigned to the considered tracks. In real conditions, the number of observations is more than targets and also locations of observations are often so scattered that the association between targets and observations cannot be done simply. In this case, for general MTT problems with unknown numbers of targets, we present a Markov chain MonteCarlo DA (MCMCDA) algorithm that approximates the optimal Bayesian filter with low complexity in computations. After DA, estimation and tracking should be done. Since in general cases, many targets can have maneuvering motions, then AIE is proposed to cover both the non-maneuvering and maneuvering parts of motion and the maneuver detection procedure is eliminated. This model with an input estimation (IE) approach is a special augmentation in the state space model which considers both the state vector and the unknown input vector as a new augmented state vector. Some comparisons based on the Monte-Carlo simulations are also made to evaluate the performances of the proposed method and other older methods in MTT.
Design of nonlinear anti-windup compensator for time-delayed systems based on triple Lyapunov Functional

In this paper, an internal model control based anti-windup compensator (AWC) is designed for nonlinear systems subjected to time-varying delay and input saturation. By using Lyapunov Krasvoskii Functional, less conservative linear matrix inequality (LMI) based delay-dependent stability condition is derived by utilizing the information of delay. Triple integral Lyapunov Functional is used for finding the gains of AWC to guarantee the closed loop stability of the system and reduce the conservatism. The saturation nonlinearity is taken into account by using sector condition. Finally, the efficacy of proposed approach is shown by numerical examples.
Malaria Parasitaemia Association with ABO Blood Types among Students of a Private University in Western Delta, Nigeria

Aim: The focus of this work was to investigate any relationship between ABO blood types and malaria parasitaemia among students of a private University based in Western Delta, Nigeria. Study Design: Whole blood samples were obtained from a randomly sampled number of students and dispensed into ethylene-diamine-tetra-acetic acid (EDTA) containers which were appropriately labelled. Collected blood samples were tested for ABO blood types and malaria parasites by standard methods. Data obtained were statistically analyzed. Place and Duration of Study: The study was carried out in the Microbiology and Biotechnology laboratory of Western Delta University, Oghara, Nigeria between May, 2013 to October, 2013. Methods: Venous blood of 2ml volume was obtained by venepuncture from 360 students made up of 150 (41.7%) males and 210 (58.3%) females of 28years average and who were both symptomatic and asymptomatic for malaria due to Plasmodium falciparum . Malaria parasite screening was done by both P. falciparum antigen rapid (Micropoint, USA) test and Giemsa staining. ABO blood typing was done using Monoclonal Antisera
Balance Dynamic Clustering Analysis and Consensus Reaching Process With Consensus Evolution Networks in Large-Scale Group Decision Making

Large-scale group decision-making (LSGDM) solution is usually based on the clustering analysis process (CAP) and consensus reaching process (CRP). However, CAP and CRP can be contradictory since CAP is performed based on the differences between potentially small groups and CRP is conducted to improve the overall similarity of a large group. To balance CAP and CRP, a dynamic clustering analysis process (DCAP) based on consensus evolution networks is proposed. A clustering algorithm proposed based on community detection method can be used to handle the diverse network structures with dynamic consensus thresholds. The clustering validity based on the intracluster consensus levels in subgroups and the intercluster consensus level among subgroups is evaluated. Then, the DCAP after each feedback adjustment round in CRP is reanalyzed. In such a way, effective clustering can also be found after a satisfying consensus is reached. Finally, a case study shows the availability of this approach and comparative analyses are provided to highlight the advantages from both theoretical and numerical perspectives.
The evolution of rogue wave triplets and super rogue waves in superthermal polarized space dusty plasma

This present investigation has been instigated to examine the impact of polarization force on modulational instability of dust acoustic (DA) waves and transition of rogue wave triplets to super rogue waves in dusty plasma composed of negative dust as fluid, Boltzmannian electrons, and superthermal ions. The presence of superthermal ions has remarkably altered the impact of polarization force. An increment in ion superthermality index restricts the polarization parameter toward smaller values. By adopting the reductive perturbation technique, the nonlinear Schrodinger equation (NLSE) is procured that determines the modulational instability of the dust acoustic waves. It is observed that the effect of polarization force is constricted by the wavenumber domain in the advent of the instability region. The rational solution of NLSE describes the evolution of dust acoustic rogue wave triplets, which further transform into the super rogue waves by means of superposition of triplets. It is remarked that the amalgamation of polarization force and superthermal ions have an explicit impact on the characteristics of different kinds of dust acoustic rogue waves. It is intensified that our present theoretical pronouncements may shed light on the salient features of different kinds of DA rogue waves in laboratory experiments and space/astrophysical regions, especially in Saturn's magnetosphere, planetary rings, and comet tails, etc.
Response of Convective Boundary Layer and Shallow Cumulus to Soil Moisture Heterogeneity: A Large‐Eddy Simulation Study

In this study, the impact of varying soil moisture heterogeneity (spatial variance and structure) on the development of the convective boundary layer and shallow cumulus clouds was investigated. Applying soil moisture heterogeneity generated via spatially correlated Gaussian random fields based on a power law model and idealized atmospheric vertical profiles as initial conditions, three sets of large‐eddy simulations provide insight in the influence of soil moisture heterogeneity on the ensuing growth of the convective boundary layer and development of shallow cumulus clouds. A sensitivity on the strong, weak, and unstructured soil moisture heterogeneity is investigated. The simulation results show that domain‐averaged land surface sensible heat and latent heat flux change strongly with changing soil moisture variance because of the interactions between surface heterogeneity and induced circulations, while domain means of soil moisture are identical. Vertical profiles of boundary layer characteristics are strongly influenced by the surface energy partitioning and induced circulations, especially the profiles of liquid water and liquid water flux. The amount of liquid water and liquid water flux increases with increasing structure. In addition, the liquid water path is higher in case of strongly‐structured heterogeneity because more available energy is partitioned into latent heat and more intensive updrafts exist. Interestingly, the increase of liquid water path with increasing soil moisture variance only occurs in the strongly structured cases, which suggests that soil moisture variance and structure work conjunctively in the surface energy partitioning and the cloud formation.
Low-Rank and Sparse Matrix Decomposition With Orthogonal Subspace Projection-Based Background Suppression for Hyperspectral Anomaly Detection

Although the low-rank and sparse matrix decomposition (LRaSMD)-based anomaly detectors can effectively extract the low-rank structure as the background component and the sparse structure as the anomaly component for anomaly detection (AD) while simultaneously considering the additive noise, the background interferences in the sparse component remain a serious problem that will increase the false alarm rate and influence the detection of real anomalies. To alleviate this issue, a novel LRaSMD with orthogonal subspace projection (OSP)-based background suppression and adaptive weighting for hyperspectral AD is proposed in this letter. Based on the fact that the background interferences in the sparse component are mainly some sparse objects with slight spectral differences from the main background, the OSP is employed to project the sparse component into the background orthogonal subspace that is estimated from the low-rank component to suppress the background interferences and highlight the anomalies. Furthermore, the low-rank component provides an effective estimation of the background statistics, which can be used to adaptively weigh the detection results. Experiments on both synthetic and real hyperspectral data sets demonstrate the effectiveness of the proposed algorithm.
Possible Explanation of the Quark Confinement and Asymptotic Freedom, and Possible Solution to the Yang-Mills Mass Gap Problem

With this work, we try to answer 3 fundamental questions that have plagued mathematicians and physicists for several decades. As known, the spontaneous symmetry breaking (SSB) and the Brout-Englert-Higgs Mechanism (BEH-M) solved the Yang-Mills Mass Gap Problem. However, various mathematicians, even prestigious ones, consider the basic assumptions of the gauge theories to be wrong, as well as in conflict with the experimental evidences and in clear disagreement with the facts, distorcing the physical reality itself. Likewise, the Quantum Fields Theory (QFT) is mathematically inconsistent, adopting a mathematical structure somewhat complicated and arbitrary, which does not satisfy the strong demands for coherence. The weakest point of the gauge theories, in our opinion, consists in imposing that all the particles must be free of an intrinsic mass (massless). On the contrary, even for the particle considered universally massless, i.e. the photon (P), our calculations show a dynamic-mass, a push-momentum (p) of 1.325⋅10−22[g⋅cm/s]. That is, an optic P hits a particle with an energy-mass greater than 100 protons rest-mass’. It is clear that if we replaced this value with the full value of the P inserted in the equations of the Perturbation Theory, QFT and Yang-Mills theories, all divergences, that is all zeroes and infinities, would suddenly disappear. Consequently, the limits imposed by the SSB disappear so that there is no longer any need to deny the mass to the Nuclear Forces bosons, including the Yang-Mills b quantum. Still, the photons (Ps) are the basis of the quantum vacuum energy, which is distributed ubiquitously, also within the intra-atomic spaces. It is likely that a lot of Ps were trapped in atomic nuclei (at the time of nucleosynthesis) and among quarks (Qs) at the time of primordial nucleonic synthesis. We believe that when Qs get too close to each other, till repelling each other (Asymptotic Freedom of Qs), this may depend on the presence of a multitude of Ps that, no further compressible, begin to exert an antigravity repulsive force, just as a Dark Energy. This limit to Compressibility (C) of the radiation is shown in equation: PV 4/3 = C, where V is the volume, and P is the Pressure of the photonic gas. Quantum Mechanics plays a crucial role, through the Uncertainty Principle, in the spatial Confinement of Qs, which have remained eternally confined in an extremely narrow space by the Strong Interaction, but in primis by the very short range (likely ≈8.44[±1.44]⋅10-16cm) and lifetime of gluon(G) which, from our calculations, is ≈2.73[±0.564]⋅10-26 sec. Therefore, a new parameter may be added to the Qs and G spatial Confinement: the b quantum or G Temporal Confinement (and of their Colours and anti-Colours).
Examination of Entropy balancing technique for estimating some standard measures of treatment effects: A simulation study

In observational studies, propensity score weighting methods are regarded as the conventional standard for estimating the effects of treatments on outcomes. We introduce entropy balancing, which despite its excellent conceptual properties, has been under-utilized in the applied studies. Using an extensive series of Monte Carlo simulations, we evaluated the performance of entropy balancing, in estimating difference in means, marginal odds ratios, rate ratios and hazard ratios. The performance of entropy balancing was relatively compared with that of inverse probability of treatment weighting using the propensity score. We found that entropy balancing outperformed the IPW method in estimating difference in means, marginal odds ratios, and hazard ratios, but when estimating marginal rate ratios, IPW performed better. Entropy balancing produced more biased estimates in many cases. However, the entropy balancing algorithm is capable of controlling bias by loosening the tightening of the pre-specified tolerance on covariate balance. We report findings as to when one technique is better than the other with no proclamation on whether one method is in every case superior to the other. Entropy balancing merits more widespread adoption in applied studies.
Prediction of turbulent heat transfer using convolutional neural networks

With the recent rapid development of artificial intelligence (AI) and wide applications in many areas, some fundamental questions in turbulence research can be addressed, such as: ‘Can turbulence be learned by AI? If so, how and why? ’ In order to provide answers to these questions, we applied deep learning to the prediction of turbulent heat transfer based only on wall information using data obtained from direct numerical simulations (DNS) of turbulent channel flow. Through this attempt, we investigated whether deep learning could help to improve our understanding of the physics of turbulent heat transfer. Under the assumption that the wall-normal local heat flux can be explicitly expressed through a multilayer nonlinear network in terms of the nearby wall-shear stresses and wall pressure fluctuations, we applied convolutional neural networks (CNNs) to predict the local heat flux. After optimizing the network hyperparameters using a grid searching method, we found that the network can predict the heat flux very accurately with a correlation coefficient of 0.980 between the DNS and the prediction by CNN for the trained Reynolds number, $Re_{\unicode[STIX]{x1D70F}}=180$ , and shows similar accuracy at a Reynolds number three times higher than the trained number. This result indicates that relationships between the local heat flux and the nearby inputs are quite insensitive to the Reynolds number within the tested range. In addition, observing the gradient maps of the trained network, we identified the part of the input data that is essential for the local heat flux prediction and the spatial relationship between the local heat flux and the nearby input fields. In addition to obtaining an understanding of the underlying physics, we investigated whether our model could be utilized for turbulence modelling.
The State of Statistics Education Research in Client Disciplines: Themes and Trends Across the University

Abstract The reform movement in statistics education has led to a revitalization of the undergraduate introductory statistics course. However, many students satisfy their degree requirements by taking statistics courses in “client departments” such as business, the social sciences, and the lab sciences, typically taught by non-statisticians. This article presents the findings of a metasynthesis of the existing literature on teaching statistics in these client disciplines to learn (1) what is currently being taught and how, and (2) the most important challenges for statistics teachers in other departments. Articles were reviewed using qualitative axial coding and quantitative text analysis to identify common research themes and ideas in the literature for each discipline. Research themes, attitudes toward statistics instruction, and pedagogical techniques were found to vary from discipline to discipline. Collaboration with instructors in other disciplines may be a welcome step toward improving statistics instruction across the university.
An adaptive second-order sliding-mode observer for permanent magnet synchronous motor with an improved phase-locked loop structure considering speed reverse

An adaptive second-order sliding-mode observer based on Super-Twisting Algorithm (STA-SMO) to estimate rotor position and speed of permanent magnet synchronous motor (PMSM) is proposed in this paper. The advantages of the proposed observer algorithm are reflected in small chattering, high tracking accuracy, good robustness to parameters change and external disturbance in wide positive and reversal speed range. The Lyapunov stability of the system is proved. A new perturbation term form is employed and according to the stable condition of STA-SMO, the adaptive sliding-mode coefficients related to absolute value of estimated speed are deduced to guarantee the performance mentioned above in wide positive and negative speed range. In order to extract rotor position and speed in both speed range from the adaptive STA-SMO, an improved conventional phase-locked loop (IPLL), which is more accurate, is studied and its non-linear dynamics are analyzed in detail to prove the effectiveness of the IPLL theoretically. In the end, the effectiveness of the adopted adaptive second-order sliding-mode observer with IPLL structure is verified through simulations. Simulation results show that the position error and chattering of the proposed adaptive observer are decreased more than 25% and 50% compared with conventional SMO and STA-SMO in wide positive and negative speed range. Meanwhile, the proposed system still has good estimation performance and strong robustness with torque and parameters variation.
Prevalence of HIV/AIDS and prevention of mother-infant transmission among pregnant and lying-in women in Hubei

Objective:To understand the infection status of HIV among pregnant and lying-in women and mother-infant transmission after conducting intervention of HIV mother-infant transmission.Methods:HIV voluntary counseling and detection among pregnant and lying-in women was carried out in Hubei,the pregnant and lying-in women and infants with positive HIV antibody received comprehensive intervention,the infants were followed up and their HIV antibodies were detected at 12 and 18 months;the women with positive HIV antibody who were found during premarital examination were followed up,then the positive pregnant and lying-in women and the infants born by them received comprehensive intervention.Results:From July 2004 to December 2009,2 288 933 pregnant and lying-in women received HIV voluntary counseling,207 women were found with positive HIV,the positive rate was 0.009%;among the women with positive HIV,the proportions of blood transmission,sex transmission and the other transmissions were 11.60%,53.63% and 10.63%,respectively;the positive rate of HIV antibody among pregnant and lying-in women kept low and steady;after adopting intervention measures,no child of HIV mother-infant transmission was found.Conclusion:In the areas with relatively low AIDS prevalence,HIV voluntary counseling services and comprehensive intervention measures for pregnant and lying-in women and infants with positive HIV antibody are helpful to grasp the infection status of HIV among local pregnant and lying-in women and reduce the rate of HIV mother-infant transmission effectively.
Correlation of Hashimoto′s thyroiditis with urine iodine level among the coastal districts in Shandong Province

The residents living in Shandong coast were investigated by random layer sampling method for the prevalence of Hashimoto′s thyroiditis (HT) and their urinary iodine level was determined. The prevalence of HT showed regional distribution, the closer they lived to the coast, the higher the prevalence rate of HT. Urinary iodine level showed positive corerelation with thyroglobulin antibody and thyroid peroxidase antibody, suggesting the urinary iodine level is better to be controlled under 300 μg/L in the residents along the coast.
Fast full-wave technique for CAD of polarizers based on double-ridge waveguide sections

ABSTRACT Full-wave technique for a computer-aided design (CAD) of a wide class of square waveguide 90-degree polarizers based on double-ridge sections is presented. The proposed hybrid full-wave technique for analysis of polarizers is based on the Galerkin method with taking into account the edge condition, mode-matching technique and generalized scattering matrix method. The weighted Gegenbauer polynomials are used as the basis functions taking into account the field asymptotic at the edges when solving the eigenvalue problem for a ridge waveguide. Compact Ku-band waveguide 90-degree polarizers are designed for operation within frequency bands 12–12.5/14–14.5 GHz and 12.1–12.5/17.7–18.1 GHz. VSWR within two operational bands of polarizers is less than 1.04, differential phase shift is about 90° ± 0.5° and 90° ± 1.5°, respectively. The length of polarizers is reduced at a factor about 2 in comparison with the known corrugated square waveguide polarizers.