Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep learning-based image segmentation model using an MRI-based convolutional neural network for physiological evaluation of the heart

Background and Objective: Cardiovascular disease is a high-fatality health issue. Accurate measurement of cardiovascular function depends on precise segmentation of physiological structure and accurate evaluation of functional parameters. Structural segmentation of heart images and calculation of the volume of different ventricular activity cycles form the basis for quantitative analysis of physiological function and can provide the necessary support for clinical physiological diagnosis, as well as the analysis of various cardiac diseases. Therefore, it is important to develop an efficient heart segmentation algorithm. Methods: A total of 275 nuclear magnetic resonance imaging (MRI) heart scans were collected, analyzed, and preprocessed from Huaqiao University Affiliated Strait Hospital, and the data were used in our improved deep learning model, which was designed based on the U-net network. The training set included 80% of the images, and the remaining 20% was the test set. Based on five time phases from end-diastole (ED) to end-systole (ES), the segmentation findings showed that it is possible to achieve improved segmentation accuracy and computational complexity by segmenting the left ventricle (LV), right ventricle (RV), and myocardium (myo). Results: We improved the Dice index of the LV to 0.965 and 0.921, and the Hausdorff index decreased to 5.4 and 6.9 in the ED and ES phases, respectively; RV Dice increased to 0.938 and 0.860, and the Hausdorff index decreased to 11.7 and 12.6 in the ED and ES, respectively; myo Dice increased to 0.889 and 0.901, and the Hausdorff index decreased to 8.3 and 9.2 in the ED and ES, respectively. Conclusion: The model obtained in the final experiment provided more accurate segmentation of the left and right ventricles, as well as the myocardium, from cardiac MRI. The data from this model facilitate the prediction of cardiovascular disease in real-time, thereby providing potential clinical utility.
Characterization of Micro-Vortex Generators in Supersonic Flows

An experimental study of several vortex generators with heights well less than that of the boundary-layer (“micro-” VGs) has been conducted in a supersonic wind tunnel to better understand vortex strength and development downstream of the device. VGs known as micro-ramps and common-flow up vane pairs of two heights were tested at Mach 1.5. The static pressure distribution and surface flow topology on the tunnel floor near the devices have been imaged, and two components of velocity in the wake have been measured using LDA. These data in combination with schlieren photographs give new insight into the flow structure generated by these devices. The development of the primary vortices generated by these devices is examined and the vortices’ evolution, strength, and trajectory are estimated as they travel downstream of the device. These devices are seen to behave similarly to those tested previously by others in subsonic flows.
Numerical comparison of some explicit time integration schemes used in DEM, FEM/DEM and molecular dynamics

Discontinua simulations are becoming an important part of Computational Mechanics to the extent that Computational Mechanics of Discontinua is becoming a separate subdiscipline of Computational Mechanics. Among the most widely used methods of Computational Mechanics of Discontinua are Discrete Element Methods, Molecular Dynamics Methods, Combined Finite‐Discrete Element Methods, DDA, Manifold Methods, etc. The common feature of all these methods is time discretization of the governing equations and the resulting mostly explicit time integration schemes. A wide range of time integration schemes is available in the literature. In this paper a comparative study of some of the most commonly used explicit time integration schemes is made in terms of accuracy, stability and CPU efficiency. The study has been performed using numerical experiments based on a one degree of freedom mass‐spring system. The results are presented as charts that can be used when deciding which scheme to use for a particular discontinua problem. Copyright © 2004 John Wiley & Sons, Ltd.
Flow in a centrifugal spectrometer

Rotational flow through narrow axial channels is considered in connection with a proposed technique to sort and separate particles according to sedimentation velocities. Nonlinear and linear axisymmetric flow through two channels connected by a slot in the vertical wall is studied numerically. A linearized formulation for the three-dimensional flow through a circumferentially blocked channel, with arbitrary positioning of the inlets and outlets, is examined analytically. Both approaches indicate that to have a sharp criteria for fractionation, the vertical shear layers on the channel walls must overlap. Otherwise, Coriolis effects, accompanying a strong azimuthal motion, make the sorting less precise. Results of an exploratory experiment with a simple two-stage machine demonstrate the feasibility of the basic process for simultaneous and continuous separation and fractionation.
Trends in digital detection for the quality and safety of herbs using infrared and Raman spectroscopy

Herbs have been used as natural remedies for disease treatment, prevention, and health care. Some herbs with functional properties are also used as food or food additives for culinary purposes. The quality and safety inspection of herbs are influenced by various factors, which need to be assessed in each operation across the whole process of herb production. Traditional analysis methods are time-consuming and laborious, without quick response, which limits industry development and digital detection. Considering the efficiency and accuracy, faster, cheaper, and more environment-friendly techniques are highly needed to complement or replace the conventional chemical analysis methods. Infrared (IR) and Raman spectroscopy techniques have been applied to the quality control and safety inspection of herbs during the last several decades. In this paper, we generalize the current application using IR and Raman spectroscopy techniques across the whole process, from raw materials to patent herbal products. The challenges and remarks were proposed in the end, which serve as references for improving herb detection based on IR and Raman spectroscopy techniques. Meanwhile, make a path to driving intelligence and automation of herb products factories.
Observer-based boundary control for 2D Burgers equation

The 2D Burgers equation has extensively been considered as a benchmark problem by flow control researchers. The flavour of the problem is its multidimensionality and non-linearity, which are the reasons for making the governing equation a good start for developing a well defined understanding of control of flows. This paper demonstrates that an infinite dimensional observer can be designed, and by the aid of the observer, a simple boundary feedback control can be achieved under severe noise and disturbance scenarios. The results are in good compliance with the theoretical claims.
Computational and Experimental Investigation of a Nonslender Delta Wing

Computational simulations have been performed for a 50-deg-sweep delta wing with a sharp leading edge at a 15 deg angle of attack and moderate Reynolds numbers of Re = 2 x 10 5 , Re = 6.2 x 10 5 , and Re = 2 x 10 6 . A sixth-order compact-difference scheme with an eighth-order low-pass filter is used to solve the Navier-Stokes equations. Turbulence modeling has been accomplished using an implicit large eddy simulation method that exploits the high-order accuracy of the compact-difference scheme and uses the discriminating higher-order filter to regularize the solution. Computations have been performed on a baseline mesh of 11.3 x 10 6 grid points and a refined mesh of 35 x 10 6 grid points. An assessment of grid resolution showed that significantly finer-scale features of the flow could be captured on the refined mesh, providing a more accurate representation of the complex, unsteady, separated flow. Comparisons are also made with high-resolution particle image velocimetry images obtained for the two lower Reynolds numbers. The numerical results are examined to provide a description of the mean and instantaneous flow structure over the delta wing, including the separated vortical flow, vortex breakdown, surface flow features, and surface boundary-layer transition near the symmetry plane. The effect of Reynolds number on each of these features is assessed.
Vision-based texture and color analysis of waterbody images using computer vision and deep learning techniques

Vision-based analysis of waterbodies can provide important information required for monitoring, analyzing, and managing water resource systems, such as visual flood detection, delineation, and mapping. Water, however, is an ornery object in image processing, as it can be found in different forms and colors in nature. This makes the detection, classification, and tracking of water in images and videos difficult for computer vision models. There are still visual differences resulting from water texture and its inherent optical properties associated with different waterbodies which can be recognized and extracted to support computer models to better analyze water images. This study aims to utilize a set of early, mid-level, and high-level vision techniques, including Gabor kernels, local binary patterns (LBPs), and deep learning (DL) models to extract and analyze water texture and color of different waterbodies in digital images. For this purpose, ATLANTIS TeXture (ATeX), an image dataset for waterbodies classification and texture analysis, was used. Models were trained for the task of classification on ATeX. Then, the performance of each model in extracting texture features was evaluated and compared. Results showed that the classification accuracy achieved by the Gabor magnitude tensor, LBP, and DL model (ShuffleNet V2 × 1.0) are 29, 35, and 92%, respectively, and thus the DL model outperforms traditional vision-based techniques. Moreover, the classification results on raw images represented by different color spaces (e.g., RGB, HSV, etc.) emphasized the importance of color information for digital image processing of water. Analyzing representative visual features and properties of different water types and waterbodies can facilitate designing a customized Convolutional Neural Networks (CNNs) for water scenes, as CNNs recognize objects through the analysis of both texture and shape clues and their relationship in the entire field of view.
A Tight Bound of Modified Iterative Hard Thresholding Algorithm for Compressed Sensing

We provide a theoretical study of the iterative hard thresholding with partially known support set (IHT-PKS) algorithm when used to solve the compressed sensing recovery problem. Recent work has shown that IHT-PKS performs better than the traditional IHT in reconstructing sparse or compressible signals. However, less work has been done on analyzing the performance guarantees of IHT-PKS. In this paper, we improve the current RIP-based bound of IHT-PKS algorithm from $${\delta _{3s - 2k}} < {1 \over {\sqrt {32}}} \approx 0.1768$$ δ 3 s − 2 k < 1 32 ≈ 0.1768 to $${\delta _{3s - 2k}} < {{\sqrt 5 - 1} \over 4}$$ δ 3 s − 2 k < 5 − 1 4 , where δ _3 s −2 k is the restricted isometric constant of the measurement matrix. We also present the conditions for stable reconstruction using the IHT^ μ -PKS algorithm which is a general form of IHT-PKS. We further apply the algorithm on Least Squares Support Vector Machines (LS-SVM), which is one of the most popular tools for regression and classification learning but confronts the loss of sparsity problem. After the sparse representation of LS-SVM is presented by compressed sensing, we exploit the support of bias term in the LS-SVM model with the IHT^ μ -PKS algorithm. Experimental results on classification problems show that IHT^ μ -PKS outperforms other approaches to computing the sparse LS-SVM classifier.
Flowfield surveys and computations of a crossing-shock wave/boundary-layer interaction

A joint experimental and computational study has been performed to investigate the flowfield structure created by two crossing oblique shock waves interacting with a turbulent boundary layer. Such an interaction is of practical importance in the design of high-speed sidewall-compression inlets. The interaction is created by a test model, consisting of two sharp fins mounted at 15-deg angle of attack to a flat plate, placed in a Mach 3.85 freestream flow with a unit Reynolds number of 76 X 106/m. Two computational solutions, one using a Baldwin-Lomax algebraic turbulent eddy viscosity model and one using a modified K-£ (Rodi) turbulence model, are compared with experimental flowfield data obtained from a fast-response five-hole probe. Both the experiment and the computations show that the flowfield is dominated by a large, low-Mach-number, low-total-pressure separated region located on the interaction centerline. A comparison of the results shows significant differences between experiment and computations within this separated region. Outside the separated region, the experiment and computations are in good agreement. Additionally, the comparison shows that both turbulence models provide similar results, with neither model being clearly superior in predicting the flowfield.
Displaying Factor Relationships in Experiments

Abstract A method of displaying the relationships between the various factors in experimental designs is described. Emphasis is placed on identifying crossed and nested factors and the experimental units for each factor. The resulting factor relationship diagram (FRD) is useful in establishing the correct model, such as analysis of variance (ANOVA), for analysis. The method is illustrated with a randomized block design and two split-plot designs. Some common pitfalls of these designs are discussed.
Enhanced energy storage performance in polypropylene-acrylic acid grafted polypropylene-ZrO2 ternary nanocomposites

To improve the energy storage density while maintaining low dielectric loss is crucial for the miniaturization of capacitors. In the present study, we proposed a ternary nanocomposite system comprised of polypropylene (PP), acrylic acid grafted PP (PP-g-AA), and nano-ZrO2. The PP-g-AA was firstly prepared by a free radical grafting with the proportion fixed to 50 wt%, and the content of nano-ZrO2 ranged from 0.5 wt% to 5 wt% to tailor the ternary composite. The transmission electron microscope observation showed that PP-g-AA could significantly benefit the dispersion of nanoparticles and the differential scanning calorimetry results indicated the superior compatibility of the ternary nanocomposites. Accordingly, with the optimum nano-ZrO2 content of 0.5 wt%, the ternary nanocomposites achieved the permittivity of 2.7 and DC breakdown strength of 456.2 kV mm−1, resulting in the enhanced energy density of 2.77 J cm−3 which was 60% higher than neat PP, while the dielectric loss was kept lower than 0.003. Moreover, even at the temperature of 120 °C, the ternary nanocomposites maintained a high-performance energy storage density of 2.28 J cm−3 (with energy storage efficiency above 90%), which was 670% of the neat PP.
Methods to evaluate arc stability in plasma arc cutting torches

Plasma cutting is commonly used to cut metals in a variety of applications. This research is motivated by the lack of fundamental understanding of the dynamics of the plasma flow interacting with cold gas environment. It is focused on the characterization of the arc instabilities that affect the quality and consistency of a cut. The characterization of instabilities has been performed with high-speed Schlieren imaging technology to visualize the arc width and the light intensity fluctuations in the arc boundary layer. Measurements of arc symmetry using Fourier and wavelet transforms of the light intensity fluctuations in individual pixels in the Schlieren images are able to provide a quantitative measure for the degree of instability. Ambient gas entrainment into the plasma jet is measured using a CCD camera with a narrow bandpass filter centred on the nitrogen atomic line to detect nitrogen concentrations throughout the arc, thus showing the penetration of shield and ambient gas into the pure oxygen plasma. The effects of torch design features on these characteristics are investigated by comparing two different torch designs. The results show that the developed diagnostics are useful for evaluating new torch designs.
LncRNA HCP5-Encoded Protein Regulates Ferroptosis to Promote the Progression of Triple-Negative Breast Cancer

Simple Summary The objective of the study was to investigate the possibility of lncRNA HCP5-encoded peptide/protein and the role of the resulting product in triple-negative breast cancer. Our study revealed that lncRNA HCP5 encodes a 132-amino acid sequence, referred to as HCP5-132aa, which enhances the growth of triple-negative breast cancer cells by regulating ferroptosis. Moreover, breast cancer patients with elevated levels of HCP5-132aa had a worse prognosis. Our findings have revealed a regulatory mechanism of ferroptosis in triple-negative breast cancer that is modulated by an lncRNA-encoded product. Abstract Background: Long non-coding RNAs (lncRNAs) are a class of RNA molecules that are longer than 200 nucleotides and were initially believed to lack encoding capability. However, recent research has found open reading frames (ORFs) within lncRNAs, suggesting that they may have coding capacity. Despite this discovery, the mechanisms by which lncRNA-encoded products are involved in cancer are not well understood. The current study aims to investigate whether lncRNA HCP5-encoded products promote triple-negative breast cancer (TNBC) by regulating ferroptosis. Methods: We used bioinformatics to predict the coding capacity of lncRNA HCP5 and conducted molecular biology experiments and a xenograft assay in nude mice to investigate the mechanism of its encoded products. We also evaluated the expression of the HCP5-encoded products in a breast cancer tissue microarray. Results: Our analysis revealed that the ORF in lncRNA HCP5 can encode a protein with 132-amino acid (aa), which we named HCP5-132aa. Further experiments showed that HCP5-132aa promotes TNBC growth by regulating GPX4 expression and lipid ROS level through the ferroptosis pathway. Additionally, we found that the breast cancer patients with high levels of HCP5-132aa have poorer prognosis. Conclusions: Our study suggests that overexpression of lncRNA HCP5-encoded protein is a critical oncogenic event in TNBC, as it regulates ferroptosis. These findings could provide new therapeutic targets for the treatment of TNBC.
Penerapan Bootstrapping untuk Ketidakseimbangan Kelas dan Weighted Information Gain untuk Feature Selection pada Algoritma Support Vector Machine untuk Prediksi Loyalitas Pelanggan

Prediksi loyalitas pelanggan merupakan sebuah strategi bisnis yang penting bagi industri telekomunikasi modern untuk memenangkan persaingan global, karena untuk mendapatkan pelanggan baru biayanya lebih mahal lima sampai enam kali lipat daripada mempertahankan pelanggan yang sudah ada. Klasifikasi loyalitas pelanggan bertujuan untuk mengidentifikasi pelanggan yang cenderung beralih ke perusahaan kompetitor yang sering disebut customer churn . Algoritma Support Vector Machine (SVM) adalah algoritma klasifikasi yang juga berfungsi untuk memprediksi loyalitas pelanggan. Penerapan algoritma SVM dalam memprediksi loyalitas pelanggan mempunyai kelemahan yang mempengaruhi keakuratan dalam memprediksi loyalitas pelanggan yaitu sulitnya pemilihan fungsi kernel dan penentuan nilai parameternya. Dataset yang besar pada umumnya mengandung ketidakseimbangan kelas ( class imbalance ), yaitu adanya perbedaan yang signifikan antar jumlah kelas, yang mana kelas negatif lebih besar daripada kelas positif. Dalam penelitian ini diusulkan metode resampling bootstrapping untuk mengatasi ketidakseimbangan kelas. Selain itu dataset juga mengandung fitur yang tidak relevan sehingga dalam pemilihan fitur dalam penelitian ini digunakan metode dua fitur seleksi yaitu Forward Selection (FS) dan Weighted Information Gain (WIG). FS berfungsi untuk menghilangkan fitur yang paling tidak relevan serta membutuhkan waktu komputasi yang relatif pendek dibandingkan dengan backward elimination dan stepwise selection. WIG digunakan untuk memberi nilai bobot pada setiap atribut, karena WIG lebih cocok digunakan dalam memilih fitur terbaik daripada Principal Component Analysis (PCA) yang biasa digunakan untuk mereduksi data yang berdimensi tinggi. Tujuan pembobotan ini untuk merangking atribut yang memenuhi kriteria ( threshold ) yang ditentukan dipertahankan untuk digunakan oleh algoritma SVM. Sedangkan untuk pemilihan parameter algoritma SVM dengan menggunakan metode grid search . Metode grid search dapat mencari nilai parameter terbaik dengan memberi range nilai parameter. Grid search juga sangat handal jika diaplikasikan pada dataset yang mempunyai atribut sedikit daripada menggunakan random search . Hasil eksperimen dari beberapa kombinasi parameter dapat disimpulkan bahwa prediksi loyalitas pelanggan dengan menggunakan sampel bootstrapping, FS-WIG serta grid search lebih akurat dibanding dengan metode individual SVM.
Large quantum probability backflow and the azimuthal angle–angular momentum uncertainty relation for an electron in a constant magnetic field

In this paper we demonstrate a surprising aspect of quantum mechanics that is accessible to an undergraduate student. We discuss probability backflow for an electron in a constant magnetic field. It is shown that even for a wavepacket composed entirely of states with negative angular momentum the effective angular momentum can take on positive values in some regions of space. This is also a demonstration of the azimuthal angle–angular momentum uncertainty relation. This example is chosen because the backflow motion persists indefinitely. However this work can easily be extended to other systems where the motion is notionally circular, such as the hydrogen atom.
ENTROPY GENERATION ANALYSIS FOR FULLY DEVELOPED TURBULENT FLOW IN TRANSITION CHANNELS

Abstract Entropy generation of fully developed turbulent flow in a transition duct is investigation in this study. Air was used to study as working flow on entropy generation, flow passing from a rectangular to square channel in turbulent flow conditions, experimentally. Entropy generation was increased according to the equivalent conical angle of the transition duct. The lateral side angle of the duct was varied to investigate its effect on the entropy generation Keywords— Channel, Entropy generation, Turbulent flow