Miracle-dz/UniFAR-FTUs huggingface dataset:
Stability of streets of vortices on surfaces of revolution with a reflection symmetry

Helmholtz’s theory of ideal vortex motion in two dimensions is generalized to flows on curved surfaces. The existence of a generalized vortex stream function is proved and used to generate conservation laws. In particular, the angular moment of circulation is related to invariance under scale transformations. The theory is used to derive criteria for stability of vortex streets on surfaces of revolution having symmetry under reflection in a plane whose normal is the axis of revolution. For the special case of the sphere it is found that only those vortex streets having six or fewer vortices per ring can be stable and that, in contradistinction to the results of von Karman, both symmetric and staggered vortex streets can be stable.
Optimization Analysis and Standardization of Rolling Process with Machine Learning

This study proposes a model based on machine learning(ML) for predicting the thickness of a coil in a steel rolling process. To achieve this objective, it is necessary to establish an engineering strategy based on data collected from actual rolling sites and to develop multiple models. Five models are trained on preprocessed process data, and the optimal model is determined through experiments.
Self-supervised Hypergraph Representation Learning

Despite the prevalence of hypergraphs in a variety of high-impact applications, there are relatively few works on hypergraph representation learning, most of which primarily focus on hyperlink prediction, and are often restricted to the transductive learning setting. Among others, a major hurdle for effective hypergraph representation learning lies in the label scarcity of nodes and/or hyperedges. To address this issue, this paper presents an end-to-end, bi-level pre-training strategy with Graph Neural Networks for hypergraphs. The proposed framework named HyperGRL bears three distinctive advantages. First, it is mainly designed in the self-supervised fashion which has broad applicability, and meanwhile it is also capable of ingesting the labeling information when available. Second, at the heart of the proposed HyperGRL are two carefully designed pretexts, one on the node level and the other on the hyperedge level, which enable us to encode both the local and the global context in a mutually complementary way. Third, the proposed framework can work in both transductive and inductive settings. When applying the two proposed pretexts in tandem, it can accelerate the adaptation of the knowledge from the pre-trained model to downstream applications in the transductive setting, thanks to the bi-level nature of the proposed method. Extensive experiments demonstrate that: (1) HyperGRL achieves up to 5.69% improvements in hyperedge classification, and (2) improves pre-training efficiency by up to 42.80% on average1.
Coupled lattice‐Boltzmann and finite‐difference simulation of electroosmosis in microfluidic channels

In this article we are concerned with an extension of the lattice‐Boltzmann method for the numerical simulation of three‐dimensional electroosmotic flow problems in porous media. Our description is evaluated using simple geometries as those encountered in open‐channel microfluidic devices. In particular, we consider electroosmosis in straight cylindrical capillaries with a (non)uniform zeta‐potential distribution for ratios of the capillary inner radius to the thickness of the electrical double layer from 10 to 100. The general case of heterogeneous zeta‐potential distributions at the surface of a capillary requires solution of the following coupled equations in three dimensions: Navier–Stokes equation for liquid flow, Poisson equation for electrical potential distribution, and the Nernst–Planck equation for distribution of ionic species. The hydrodynamic problem has been treated with high efficiency by code parallelization through the lattice‐Boltzmann method. For validation velocity fields were simulated in several microcapillary systems and good agreement with results predicted either theoretically or obtained by alternative numerical methods could be established. Results are also discussed with respect to the use of a slip boundary condition for the velocity field at the surface. Copyright © 2004 John Wiley & Sons, Ltd.
Extreme Ultraviolet Line Emission from Cool Stars: Resonantly Scattered or Not—That Is the Question!

A challenge to the classical assumption that the radiative losses from stellar coronae are optically thin has been raised by Schrijver, van den Oord, & Mewe, who argued that some of the stronger emission lines detected in the high-resolution spectra of cool stars observed with the Extreme Ultraviolet Explorer (EUVE) satellite are optically thick. If they assume all radiation optically thin, an explanation of the short-wavelength (SW) part of the EUVE spectrum requires large amounts of emission measure at very high temperatures (T ≈ 108 K), which is unlikely the case for rather inactive stars. We show that the soft X-ray pulse height spectrum obtained with the Position Sensitive Proportional Counter (PSPC) on board ROSAT is inconsistent with such high-temperature emission and inconsistent with the assumption that the EUV line emission is optically thick. We further demonstrate via an analysis of fit residuals that the observed count fluctuations in the EUVE SW spectrum are inconsistent with the hypothesis that the bulk of the observed flux arises from a continuum. Therefore, resonance scattering does not appear to be required for the interpretation of the EUV and X-ray spectra of inactive cool stars.
Place-based public investment in regional infrastructure, the locational choice of firms and regional performance: the case of India

ABSTRACT We discuss conceptual reasons for and propose public investment in regional infrastructure as a hybrid form of a place-based regional industrial policy aiming to foster the regional economic activity of lagging regions. We present and empirically test a baseline model using data for 14 Indian regions/states over a period of 39 years. Our results show that place-based regional infrastructure investments, particularly in electricity-generating capacity and width-adjusted length of national highways, positively impact the relative number of firms operating in a region and help foster its economic performance.
Spherical needlets for cosmic microwave background data analysis

We discuss Spherical Needlets and their properties. Needlets are a form of spherical wavelets which do not rely on any kind of tangent plane approximation and enjoy good localization properties in both pixel and harmonic space; moreover needlets coefficients are asymptotically uncorrelated at any fixed angular distance, which makes their use in statistical procedures very promising. In view of these properties, we believe needlets may turn out to be especially useful in the analysis of Cosmic Microwave Background (CMB) data on the incomplete sky, as well as of other cosmological observations. As a final advantage, we stress that the implementation of needlets is computationally very convenient and may rely completely on standard data analysis packages such as HEALPix.
Authentication of the botanical origin of unifloral honey by infrared spectroscopy coupled with support vector machine algorithm

In recent years, the potential of Fourier-transform infrared spectroscopy coupled with different chemometric tools in food analysis has been established. This technique is rapid, low cost, and reliable and requires little sample preparation. In this work, 130 Serbian unifloral honey samples (linden, acacia, and sunflower types) were analyzed using attenuated total reflectance infrared spectroscopy (ATR-IR). For each spectrum, 64 scans were recorded in wavenumbers between 4000 and 500 cm−1 and at a spectral resolution of 4 cm−1. These spectra were analyzed using principal component analysis (PCA), and calculated principal components were then used for support vector machine (SVM) training. In this way, the pattern-recognition tool is obtained for building a classification model for determining the botanical origin of honey. The PCA was used to analyze results and to see if the separation between groups of different types of honeys exists. Using the SVM, the classification model was built and classification errors were acquired. It has been observed that this technique is adequate for determining the botanical origin of honey with a success rate of 98.6%. Based on these results, it can be concluded that this technique offers many possibilities for future rapid qualitative analysis of honey.
Representing capabilities of novel semi-analytical triangular plate elements

Two novel plate-bending elements are developed and investigated in this study. Elements with 13 and 15 degree-of-freedoms are named AT13 and AT15, respectively. These triangular elements are formulated in a semi-analytic way. For this aim, the basic elasticity function is employed with unknown parameters. Subsequently, the trial-and-error procedure is used to determine the unidentified constants. Besides, the achieved results are compared with those obtained by displacement-based triangular elements with the same degrees-of-freedom (TUBA13 and TUBA15). In this research, both stress and displacement responses of diverse structures are assessed. After performing extensive numerical studies, the findings clearly demonstrate the superiorities of the proposed elements.
Theory, Design, and Verification of Miniaturized Phase-Controlled Huygens Antenna for 360° Continuous Beam Scanning

The theory, design, and verification of a novel miniaturized reconfigurable Huygens antenna with 360° continuous beam-scanning capability are demonstrated in this article. Different from the conventional Huygens topology, both the monopole-like Huygens form and the metamaterial loading technique are explored for size reduction. By creatively tuning the direction (or polarization) of the electric dipole (E-dipole) or magnetic dipole (M-dipole), a novel miniaturized pattern reconfigurable Huygens source is first proposed. Then, to control the polarization of the M-dipole [linearly polarized (LP)], a double circularly polarized (CP) wave with a tunable phase difference is introduced. Furthermore, a novel phase-controlled Huygens mechanism is developed by combining the polarization reconfigurable M-dipole and fixed E-dipole. By modulating the feeding phase of the Huygens source, the beam can be steered in any direction in the horizontal plane. The proposed theory and working principle are verified by a metamaterial-loaded tri-mode dielectric resonator radiator and a three-way phase-shifting network. The antenna is operated from 3.40 to 3.68 GHz and is capable of continuously scanning a directional beam from 0° to 360°. The realized peak gain and 3 dB beamwidth are greater than 6.3 dBi and 90°, respectively. This work proposes a new realization method for a simple, low-cost, miniaturized, vertically polarized (VP), 360° continuous beam-steering antenna. It is promising for intelligent wireless communications, the Internet-of-Things (IoT) devices, and DOA estimation applications.
MeWP: Meta-learning based Water-Level Prediction

Recently, extreme heat waves have swept the world, exacerbating the pressing issue of increased drought occurrences. Accurate water level prediction is crucial in combating droughts. However, current water level prediction models lack interchangeability between different bodies of water and ultimately fail to consider the relevance between different hydrological systems, rendering them inaccurate and ineffective. Therefore, a generalized, adaptable, and reliable water level prediction model is crucial.In this paper, we propose a meta-learning based model called MeWP to address such issues. To incorporate meta-learning methods, we initially embed and compile different lake data and subsequently generate global optimal initial parameters. We also design a scalable meta-trained learning based on dataset size to assist convergence efficiency on different water systems. The conclusions of our extensive experiments on five public datasets demonstrate the superior stability, accuracy, and flexibility of MeWP compared to other state-of-the-art methods.
Overcoming Immune Deficiency with Allogrooming

Simple Summary We investigated whether two closely related termite species differ in their reliance on social or individual immunity against two species of pathogenic fungi, which were all collected from the same location. Our results indicate that mutual grooming (allogrooming) is highly effective at limiting fatal infections to the extent that it can compensate for relatively weak individual immune defenses. After pathogens are encountered, termites use an alternate strategy for social distancing and limiting the spread of contagious disease and instead come together to clean each other. Variation in the intensity of this behavioral response indicates a nuanced response to the level of threat. Abstract Allogrooming appears to be essential in many social animals for protection from routine exposure to parasites. In social insects, it appears to be critical for the removal of pathogenic propagules from the cuticle before they can start an infectious cycle. For subterranean termites, this includes fungal spores commonly encountered in the soil, such as Metarhizium conidia, that can quickly germinate and penetrate the cuticle. We investigated whether there is a difference in reliance on social and innate immunity in two closely related subterranean termites for protection from fatal infections by two locally encountered Metarhizium species. Our results indicate that relatively weak innate immunity in one termite species is compensated by more sustained allogrooming. This includes enhanced allogrooming in response to concentrations of conidia that reflect more routine contamination of the cuticle as well as to heavy cuticular contamination that elicits a networked emergency response.
Research on Coverage Enhancement and Sum Rate Maximization of 5G NOMA Network Based on Multiple IRSs

Intelligent reflecting surface (IRS) and non-orthogonal multiple access (NOMA) are new revolutionary technologies for the 5G and forthcoming wireless communication period. IRS is an effective means to improve weak coverage for that inadequate coverage will lead to signal quality degradation and low service transmission rate. In this paper, considering mobile users with variable location, to overcome the challenge that traditional IRS-assisted NOMA system with a single IRS cannot cover all mobile users, a new multiple IRS-assisted downlink NOMA network is proposed to improve system performance. The base station (BS) transmits overlay signals to multiple users with variable location through multiple IRSs reflecting elements. We maximize the sum rate by optimizing the phase shift matrix of IRSs. For the optimization problem is non convex, the semidefinite relaxation (SDR) and Gauss randomization methods are applied to solve it. The simulation results indicate that the multiple IRSs-NOMA system with optimal reflection phase can extend the coverage and improve the system performance.
LASSO Logic Engine: harnessing the logic parsing capabilities of the LASSO algorithm for longitudinal feature learning

Longitudinal data, which is widely used in many disciplines to study cause and effect, poses significant computational challenges to both modeling and analysis. Longitudinal data is composed of readings on the same variable collected over time and is often high-dimensional with correlated features. The combinatorial search approach for identifying the optimal features is unrealistic for most applications. The alternative approaches, such as heuristics, greedy searches, and regularization techniques, including LASSO, can result in models that suffer from both low accuracy and unclear feature attribution. In this paper, we propose a binary transformation on the data before applying LASSO for feature learning. As demonstrated in the paper, the binary transformation enhances signal in the data, resulting in highly accurate feature attribution, including associated time lags. It avoids the typical shortcomings of the LASSO algorithm, including saturation of the feature space and arbitrary or inconsistent sparse feature selection. Both synthetic data and real-world data sets were used to demonstrate the value of the proposed transformation and in all cases substantial improvements in feature learning were seen. In addition, the scalable parallelism of the solution is superior to that of the standard LASSO since transformation itself occurs in linear time and computing the LASSO solution using the transformed data results in a speedup of almost double.
Random sequential adsorption of hard discs and squares: exact bounds for the covering fraction

We investigate the random sequential adsorption of hard discs and hard aligned squares onto a plane, by a new series expansion method that we previously devised for a lattice. The method yields sequences of increasing lower bounds for the time-dependent covering fraction Theta (t). These bounds have non-trivial limit values for t to infinity , i.e. for the saturated state. Hypercubes and an analytic continuation in the dimension D around D=0 are also considered. Numerical results are given for the lowest orders in the series.
A dynamic community evolution discovery method based on attribute network representation learning

Mining the community structure in real-world networks has always been a popular topic in the complex network field. With the development of complex network research, the study of dynamic network community evolution discovery has become an important field and continues to develop. However, the current community discovery methods are mainly applied to static networks, and some methods applied to dynamic networks have the following problems: 1. The same community structure is divided into different time slices with low accuracy; 2. The time efficiency of the algorithm is low. Therefore, this paper proposes a dynamic community evolution discovery method based on attribute network representation learning. The method uses neural networks to couple the network structure and node information, obtain the representation vector of the node, then divides the community through the incremental community discovery method, finally tests the method on the artificial network and the real network. Compared with other algorithms in the artificial data set, the method in this paper has an average improvement of 4.1% in the evaluation index; in the real data set, the evaluation index has an average improvement of 3.8%, and the time efficiency has also been greatly improved.
A Real-time, Scalable Monitoring and User Analytics Solution for Microservices-based Software Applications

Monitoring the execution of distributed microservices-based software applications is a complex task. As more and more institutions conduct business in a distributed environment, a large amount of user data and transaction data are generated at an accelerated rate in such an environment. As a result, it becomes a big challenge to carry out user analytics with user interaction and transaction data in real-time. Monitoring is one of the most important approaches to getting instant situations of users and transactions. Ideally, the goal of monitoring is to carry out analysis in real-time and be highly scalable with comprehensive analysis and predictions on user interaction and transaction data to gain deeper and facilitate decision-making for the stakeholders. Therefore, we concentrate on what data can be captured to gain insight into users using the platform and how we can capture such data effectively to learn application usage patterns. To achieve this goal, (1) we present how different events data be captured from execution logs of software applications in distributed microservices, and (2) we utilize machine learning methods to predict usage patterns which are generated from interaction between user and system. For example, we show how error types can be predicted in real-time using machine learning and then enable real-time monitoring. The capability to perform analysis based on the continuously growing data volume is considered to be our solution's scalability characteristic. Microservices are used as containers that are managed by Kubernetes. Event logs generated in each microservice are the important data source for executing monitoring. The experiment shows our system can monitor user actions and transactions in real time and that the model capacity is scalable.
Development of Capillary Zone Electrophoresis Method for the Simultaneous Separation and Quantification of Metformin and Pioglitazone in Dosage Forms; and Comparison with HPLC Method

A capillary zone electrophoretic (CZE) method was developed, validated, and applied for the assay of metformin (MET) and pioglitazone (PIO) in pharmaceutical formulations. The optimum running buffer composition was found to be 75 mmol/L phosphate buffer containing 30% acetonitrile (ACN) at pH 4.0. The optimum instrumental conditions were found to be injection time, 10 s; applied voltage, 25 kV; hydrodynamic injection pressure, 0.5 psi for 10 s, capillary temperature, 25 °C; and the detection wavelength, 210 nm. The quantifications were calculated based on the ratio of the peak areas of analytes to atenolol as an internal standard. The CZE method was validated in terms of accuracy (98.21–104.81%), intra- and inter-day precision of migration time and peak area (relative standard deviation ≤ 5%), linearity (correlation coefficients ≥ 0.9985), limit of detection (≤0.277 μg/mL), and limit of quantitation (≤0.315 μg/mL). The proposed method was applied for the analysis of PIO and MET both individually and in a combined dosage tablet formulation. All electrophoretic parameters were calculated and evaluated. A previously reported high-performance liquid chromatographic (HPLC) method was also applied to the same samples. A comprehensive comparison was then carried out for the analytical features of both methods CZE and HPLC. Comparable results were obtained with the advantage of reagent consumption and separation efficiency of CZE over HPLC and shorter analysis time by HPLC compared with CZE.
Estimation of the Electrical Conductivity of Human Tissue

Human tissue consists of cells suspended in an aqueous medium. As a consequence, the electrical conductivity of a tissue is inhomogeneous on a microscopic scale. On a macroscopic scale the conductivity of a tissue can be considered to be homogeneous, and this conductivity is called the effective conductivity. In this paper a theory is presented to estimate this effective conductivity. On a cellular scale, the tissue will be modelled as a suspension of ellipsoidal cells. On a somewhat larger scale, it will be modelled as a layered structure, as most tissues can be described by layers that differ in conductivity. For both scales, upper and lower bounds for the effective conductivity will be estimated. The more information on the shape, orientation, conductivities, and distribution of the cells or layers is available, the more strict these limits are. The effective conductivity will be evaluated for several tissues, such as the cerebral cortex, the liver, and blood.
Analytical calculation of the Q factor for circular-grating microcavities

An exact and compact analytical formalism has been developed to calculate the Q factor for circular Bragg resonators. The electromagnetic fields, energy, and power flow have been expressed analytically relying on the transfer matrix coefficients. The Q factor has been derived for both TM and TE polarizations. The proposed formalism is then compared with two numerical methods.