Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of Different Turbulence Models for Duct Flow Simulation With Reduced and Full Navier-Stokes Equations

An S-shaped diffusing duct flow is analysed with various turbulence models: standard high-Reynolds-number k-e model with wall functions, a low-Reynolds-number k-e model, and an explicit non-linear algebraic Reynolds stress closure model (ASM). In addition, computations were obtained with parabolic and partially-parabolic approximations along with solutions of the full Navier-Stokes equations. Results of the numerical simulation are compared with LDA measurements of the turbulent flow as reported by Whitelaw and Yu (1993). Detailed comparisons of mean velocity and Reynolds stress distributions are presented. It is shown that the partially-parabolic approach gives a significant improvement over parabolic approximation in predicting mean velocities and pressure distributions. However, the turbulence models used are still not able to reproduce all the observed flow features, although the ASM results appear to be slightly but consistently closer to the experimental data.Copyright © 1995 by ASME
STELLAR MULTIPLICITY OF THE OPEN CLUSTER MELOTTE 111

We present a high-angular resolution survey for the search of binary and multiple stars in the Galactic open cluster Melotte 111. Combining our own speckle-interferometric observations with data taken from the literature, we found a ratio of the number of single to multiple stars to be 29:8:0:0:0:1 for the most probable members, indicating a multiplicity fraction for this cluster of 25.6% ± 2%. We also observed field stars in the vicinity of the cluster and estimated a ratio of multiplicities of 286:17:1:0:0:0:1 (between one and seven companions), equivalent to a multiplicity fraction of 5.9% ± 3%. We showed that the cumulative distribution of separations for our sample stars is in agreement with Öpik’s law.
Assessment of Stress Distribution Around Dental Implants Based on the All-on-Four Treatment Concept using Finite Element Analysis

The present study was conducted to assess stress distribution around dental implants based on the all-on-four treatment concept. The finite element analysis (FEA) models comprised cancellous bone covered with cortical bone. Four dental implants were placed in two different designs. In model 1, the four implants were placed parallel to each other, whereas, in model 2, the all-on-four concept was followed. The vertical and lateral loads of various values were applied, and stress was evaluated. In model 2, the least stress was observed in both lateral and vertical loading in the peri-implant region. The all-on-four concept proved to be beneficial in reducing the stress around dental implants, thereby reducing the treatment cost.
Integrated bioinformatics analysis to identify key genes and pathways involved in the endometria of patients with recurrent implantation failure undergoing in vitro fertilization and embryo transfer

Objectives: Recurrent implantation failure (RIF) is a challenging clinical problem that occurs in approximately 10% of patients undergoing in vitro fertilization and embryo transfer. Because of the varied criteria for RIF, as well as differences in the time and methods of endometrial biopsy, previous studies have not reached an agreement regarding the key genes and pathways of RIF. In this study, bioinformatic analyses were performed in a homogeneous population with consistent biopsy conditions to explore the molecular mechanisms and identify key RIF-associated genes and pathways in the endometrium. Methods: Seven datasets from the Gene Expression Omnibus database were reviewed in detail, and GSE111974, GSE103465, and GSE26787 were included for further analysis. Common differentially expressed genes (DEGs) were identified using the GEO2R online tool. Gene Ontology and pathway enrichment analyses were performed and a protein–protein interaction (PPI) network was constructed. The RNA and protein expression files of the hub genes were obtained. Results: A total of 402 common DEGs were identified between patients with RIF and controls. The enrichment of these DEGs was mainly associated with tube morphogenesis, vasculature development, and positive regulation of cell migration. Pathway enrichment indicated that pathways involved in cancer, the vascular endothelial growth factor A-vascular endothelial growth factor receptor 2 signaling pathway, and cell cycle pathways were mainly involved. Moreover, a PPI network was constructed, and the three most significant submodules were mainly involved in rRNA processing and the regulation of proteolysis. The following ten DEGs with high degrees of connectivity were identified as hub genes: CDC42, VEGFA, MAP2K1, APP, CYCS, UBE2I, PLEK, LMNA, SMAD2, and ATRX. Conclusion: Key genes and pathways identified in this study could improve our understanding of the underlying molecular events in RIF and be used as potential biomarkers and therapeutic targets. Further studies are required to confirm these results and explore the functions of hub genes in the endometrium during the implantation window in patients with RIF.
Feasibility for Orbital Life Extension of a CubeSat in the Lower Thermosphere

Orbital flight of CubeSats at altitudes between 150 and 250 km has the potential to enable a new class of scientific, commercial, and defense-related missions. A study is presented to demonstrate the feasibility of extending the orbital lifetime of a CubeSat in a 210 km orbit. Propulsion consists of an electrospray thruster operating at a 2 W, 0.175 mN thrust, and an specific impulse (Isp) of 500 s. The mission consists of two phases. In phase 1, the CubeSat is deployed from a 414 km orbit and uses the thruster to deorbit to the target altitude of 210 km. In phase 2, the propulsion system is used to extend the mission lifetime until propellant is fully expended. A control algorithm based on maintaining a target orbital energy is presented that uses an extended Kalman filter to generate estimates of the orbital dynamic state, which are periodically updated by Global Positioning System measurements. For phase 1, the spacecraft requires 25.21 days to descend from 414 to 210 km, corresponding to a ΔV=96.25 m.
Supermagic Complete Graphs

In our paper “Magic graphs” (1) we showed that every complete graph Kn with n ⩾ 5 is “magic,” i.e., if the vertex set is indicated {vi} and if eij is the edge joining vi and vj, i ≠ j , then there exists a function α(eij) such that the set {α(eij)} consists of distinct positive rational integers and the vertex sums 1 have a constant value σ(α) for k = 1, 2, … , n. We noted that K 2 is magic and showed that K 3 and K 4 are not magic.
Representation Learning of Clinical Multivariate Time Series with Random Filter Banks

Machine learning and deep learning models for time series classification generally require a large volume of data to achieve superior performance. However, due to the lack of a sufficient amount of time series in many real-world applications, particularly health care, training these models is more challenging than expected. This paper introduces the Random Frequency Butchering (RFB) method to enhance the generalization performance of classification tasks on limited time series in health care. This approach generates a number of filters with random cutoff frequencies in the frequency domain. The concatenation of time series representations from these filters stacked with the original time series is then used to train an arbitrary time series classifier. The experimental results on the standard medical time series datasets show that the RFB time series representation can significantly enhance the classification performance of the MiniRocket, Inception-Net, and ResNet classifiers.
GNSS/INS/OD/NHC Adaptive Integrated Navigation Method Considering the Vehicle Motion State

Global navigation satellite systems (GNSSs) integrated with inertial navigation systems (INSs) have been widely applied in many intelligent transport systems. At present, integrating GNSS, microelectromechanical system (MEMS), on- board controller area network (CAN) sensors, and vehicle motion constraint information is the most practical and low-cost vehicle multifusion navigation scheme when GNSS outages. It is especially true when using 3-D velocity from nonholonomic constraints (NHC) and odometer (OD). The GNSS/INS/OD/NHC integration, however, has the problem of inaccuracy in lateral velocity constraint parameters. To overcome this problem, a back propagation (BP) neural network-based GNSS/INS/OD/NHC adaptive integrated navigation method considering the vehicle motion state is proposed in this article. The relationship between the forward velocity, the heading angular velocity, and the lateral velocity is analyzed and considered when the NHC lateral velocity constraint modeling is established by using the BP neural network. To assess the performance of this method, three sets of real land vehicle data are tested with intentional GNSS signal interruption at different vehicle states. The performances of the classic INS/NHC model, INS/OD/NHC integration, and the proposed method are compared, respectively. Experimental results show that the mean error and RMSE of the lateral velocity predicted by the proposed method is 0.007 and 0.049 m/s. The mean 3-D RMSE of the positioning errors and the velocity errors of the proposed method is 1.515 m and 0.182 m/s respectively, which are improved by 54.86% and 44.85% compared with that of the classic INS/OD/NHC integration.
Cellular Mechanisms Mediating Exercise-Induced Protection against Cardiotoxic Anthracycline Cancer Therapy

Anthracyclines such as doxorubicin are widely used chemotherapy drugs. A common side effect of anthracycline therapy is cardiotoxicity, which can compromise heart function and lead to dilated cardiomyopathy and heart failure. Dexrazoxane and heart failure medications (i.e., beta blockers and drugs targeting the renin–angiotensin system) are prescribed for the primary prevention of cancer therapy-related cardiotoxicity and for the management of cardiac dysfunction and symptoms if they arise during chemotherapy. However, there is a clear need for new therapies to combat the cardiotoxic effects of cancer drugs. Exercise is a cardioprotective stimulus that has recently been shown to improve heart function and prevent functional disability in breast cancer patients undergoing anthracycline chemotherapy. Evidence from preclinical studies supports the use of exercise training to prevent or attenuate the damaging effects of anthracyclines on the cardiovascular system. In this review, we summarise findings from experimental models which provide insight into cellular mechanisms by which exercise may protect the heart from anthracycline-mediated damage, and identify knowledge gaps that require further investigation. Improved understanding of the mechanisms by which exercise protects the heart from anthracyclines may lead to the development of novel therapies to treat cancer therapy-related cardiotoxicity.
Utility Polelocalization by Learning from Ambient Traces on Distributed Acoustic Sensing

Utility pole detection and localization is the most fundamental application in aerial-optic cables using distributed acoustic sensing (DAS). The existing pole localization method recognizes the hammer knock signal on DAS traces by learning from knocking vibration patterns. However, it requires many efforts for data collection such as knocking every pole and manually labeling the poles’ locations, making this labor-intensive solution expensive, inefficient, and highly error-prone. In this paper, we propose a pole localization solution by learning the ambient data collected from a DAS system, which are vibration patterns excited by random ambient events, such as wind and nearby traffic. In detail, we investigate a universal framework for learning representations of ambient data in the frequency domain by contrastive learning of the similarity of low and high-frequency series. A Gaussian-based data reweighting kernel is employed for eliminating the effect of the label noise. Experimental results demonstrate the proposed methods outperform the existing contrastive learning methods on the real-world DAS ambient dataset.
SNOWED: Automatically Constructed Dataset of Satellite Imagery for Water Edge Measurements

Monitoring the shoreline over time is essential to quickly identify and mitigate environmental issues such as coastal erosion. Monitoring using satellite images has two great advantages, i.e., global coverage and frequent measurement updates; but adequate methods are needed to extract shoreline information from such images. To this purpose, there are valuable non-supervised methods, but more recent research has concentrated on deep learning because of its greater potential in terms of generality, flexibility, and measurement accuracy, which, in contrast, derive from the information contained in large datasets of labeled samples. The first problem to solve, therefore, lies in obtaining large datasets suitable for this specific measurement problem, and this is a difficult task, typically requiring human analysis of a large number of images. In this article, we propose a technique to automatically create a dataset of labeled satellite images suitable for training machine learning models for shoreline detection. The method is based on the integration of data from satellite photos and data from certified, publicly accessible shoreline data. It involves several automatic processing steps, aimed at building the best possible dataset, with images including both sea and land regions, and correct labeling also in the presence of complicated water edges (which can be open or closed curves). The use of independently certified measurements for labeling the satellite images avoids the great work required to manually annotate them by visual inspection, as is done in other works in the literature. This is especially true when convoluted shorelines are considered. In addition, possible errors due to the subjective interpretation of satellite images are also eliminated. The method is developed and used specifically to build a new dataset of Sentinel-2 images, denoted SNOWED; but is applicable to different satellite images with trivial modifications. The accuracy of labels in SNOWED is directly determined by the uncertainty of the shoreline data used, which leads to sub-pixel errors in most cases. Furthermore, the quality of the SNOWED dataset is assessed through the visual comparison of a random sample of images and their corresponding labels, and its functionality is shown by training a neural model for sea–land segmentation.
Predictive Skim: Contrastive Predictive Coding for Low-Latency Online Speech Separation

In online speech separation, there is a trade-off between inherent latency and speech separation performance. When processing the current input audio, looking ahead to more future context usually brings better speech separation performance but increases the algorithm latency, and vice versa. In the requirements of extremely low latency, the future context is expensive for the algorithm latency and may not be available. In this work, we apply the contrastive predictive coding (CPC) method to the previously proposed online Skipping Memory (SkiM) speech separation model, which is a low-latency model for online speech separation. During the training stage, the SkiM model is required to predict the future memory states given the history memory. By using CPC training, the predictive SkiM model shows stronger causal sequence modeling capacity in the online speech separation task. In addition, we explore a local context codec (LCC) method to reduce the computational cost, and we make qualitative analyses on it. Our best online predictive SkiM equipped with CPC and LCC gets 15.5 dB SI-SNR improvement on WSJ02-mix benchmark with 3-ms actual latency tested on a single-core CPU, which should be the state-of-the-art results among causal models.
A review and meta-analysis of conventional sternotomy versus minimally invasive mitral valve surgery for degenerative mitral valve disease focused on the last decade of evidence.

OBJECTIVES Early meta-analyses comparing minimally invasive mitral valve surgery (MIMVS) with conventional sternotomy (CS) have determined the safety of MIMVS. We performed this review and meta-analysis based on studies from 2014 onwards to examine the differences in outcomes between MIMVS and CS. Specifically, some outcomes of interest included renal failure, new onset atrial fibrillation, mortality, stroke, reoperation for bleeding, blood transfusion and pulmonary infection. METHODS A systematic search was performed in six databases for studies comparing MIMVS with CS. Although the initial search identified 821 papers in total, nine studies were suitable for the final analysis. All studies included compared CS with MIMVS. The Mantel - Haenszel statistical method was chosen due the use of inverse variance and random effects. A meta-analysis was performed on the data. RESULTS MIMVS had significantly lower odds of renal failure (OR: 0.52; 95% CI 0.37 to 0.73, p < 0.001), new onset atrial fibrillation (OR: 0.78; 95% CI 0.67 to 0.90, p < 0.001), reduced prolonged intubation (OR: 0.50; 95% CI 0.29 to 0.87, p = 0.01) and reduced mortality (OR: 0.58; 95% CI 0.38 to 0.87, p < 0.01). MIMVS had shorter ICU stay (WMD: -0.42; 95% CI -0.59 to -0.24, p < 0.001) and shorter time to discharge (WMD: -2.79; 95% CI -3.86 to -1.71, p < 0.001). CONCLUSION In the modern era, MIMVS for degenerative disease is associated with improved short-term outcomes when compared to the CS.
Influence of Seasons and Tides on the Distribution of Enteric Protozoa on the Shores of the Atlantic Ocean in Kribi (South Region of Cameroon): Health Risks Related to Bathing

Beaches are major sites of microbiological pollution. Assessment of the abundance of resistant forms of enteric protozoa on these recreational waters is important for the prevention and management of health risks. Based on sedimentation and flotation methods, this study found that Kribi beach waters concentrate considerable amounts of enteric protozoa, which are potentially pathogenic. They include Coccidia (Cryptosporidium sp. and Cyclospora cayetanensis), Amoebae (Endolimax nana, Entamoeba histolytica and Entamoeba coli) and Flagellates (Giardia intestinalis). In general, seasonal changes and tidal cycles have significantly impacted the spread of these parasites along Kribi beaches. Thus, at all sites surveyed (Mpalla, Ngoyè and Mboamanga), maximum protozoan abundances were recorded at low tide and during the rainy seasons. It should also be noted that at each sampling site, significant correlations were recorded between certain protozoa and certain physico-chemical variables (p < 0.05). At Mboamanga, for example, Cryptosporidium sp. and Endolimax nana were positively correlated during the Short Rainy Season with temperature (r = 0.601, p = 0.044 and r = 0.632, p = 0.042). At Mpalla, a positive and significant correlation was observed during the Short Rainy Season between Entamoeba coli and pH (r = 0.605, p = 0.033). The high concentration of resistant forms of these enteric protozoa at Kribi beaches is a real public health threat for bathers. Therefore, in this tourist town, it is urgent to put in place an effective plan for the collection and sustainable treatment of solid and liquid waste, which are the main sources of contamination.
An Improved NMM Method for Electro-Magnetic Field Calculation in Geophysics

A new type of numerical mode-matching(NMM) theory is applied into numerical simulation of common resistivity logs.In analytical section,by the continuity of interface between the adjacent layers in electromagnetic field,the traditional reflection and transmission matrixes is improved,and relationship between upgoing and down-going wave is derived.A new transition formula is firstly presented.The improved NMM method,avoiding complicated matrix inverse,is easy to understand and has concise physical meaning.Furthermore,according to reciprocal theory,the calculating speed for B-A-M is one time faster than that for A-M-N.By the experiments for two-layer and three-layer homogeneous media,this method is verified to be highly precise and effective comparing with FEM method and the previous NMM method.The core concept of new NMM is helpful to 3-dimension NMM and other geophysics method in stratum media.
Various Performance Bounds on the Estimation of Low-Rank Probability Mass Function Tensors from Partial Observations

Probability mass function (PMF) estimation using a low-rank model for the PMF tensor has gained increased popularity in recent years. However, its performance evaluation relied mostly on empirical testing. In this work, we derive theoretical bounds on the attainable performance under this model assumption. We begin by deriving the constrained Cramér-Rao Bound (CCRB) on the low-rank decomposition parameters, and then extend the CCRB to bounds on the mean square error in the resulting estimates of the PMF tensor’s elements, as well as on the mean Kullback-Leibler divergence (KLD) between the estimated and true PMFs. The asymptotic tightness of these bounds is demonstrated by comparing them to the performance of the Maximum Likelihood estimate in a small-scale simulation example.
CryoSWD: Sliced Wasserstein Distance Minimization for 3D Reconstruction in Cryo-electron Microscopy

Single particle reconstruction (SPR) in cryo-electron microscopy (cryo-EM) is a prominent imaging method that recovers the 3D shape of a biomolecule, given a large number of its noisy projections from random and unknown views. Recently, CryoGAN [1] cast SPR as an unsupervised distribution matching problem and solved it via a Wasserstein generative adversarial network (WGAN) framework. The approach bypasses the estimation of the projection parameters. The reconstruction criterion in CryoGAN is Wasserstein-1 distance. Despite the desirable properties of Wasserstein distances (WD) such as continuity and almost everywhere differentiability, they are difficult to compute and require careful tuning for a stable training. Sliced Wasserstein distance (SWD), on the other hand, has shown desirable training stability and ease to compute. Therefore, we propose to re-place Wasserstein-1 distance with SWD in the CryoGAN framework, hence the name CryoSWD. In low noise regimes, we show how CryoSWD eliminates the need to have a discriminator which is crucial in CryoGAN. However, coupling CryoSWD with a discriminator boosts its performance, especially in high noise settings. While performing as good as CryoGAN, CryoSWD does not require a gradient penalty term for stabilizing the training and imposing Lipschitz continuity of the discriminator.
A novel fabric defect detection algorithm based on textural differential visual saliency model

In order to effectively detect defect for fabirc image with variety of defects and complex texture,a novel fabric defect detection scheme based on textural difference-based visual saliency model was proposed,which considered the characteristics of fabric image and human visual perception. First,the test image was split into image blocks,and textural feature was extracted using LBP operator for each image block. Second,saliency was calculated by comparing their textural feature with the average texture feature. Finally,the threshold segmentation algorithm was used to localize the defect region. Comparing with the current saliency model,the proposed saliency model could effectively distinguish the defect. In addition,segmentation scheme was superior to the current defect detection algorithm in detection and localization.