Miracle-dz/UniFAR-FTUs huggingface dataset:
Wave radiation by a horizontal circular cylinder in a three-layer fluid

To construct cylindrical structures like circular pipe bridges or tunnels submerged in the almost still density-stratified ocean or seawater, the study of waves radiated by the cylinder is essential. This research solves the wave radiation problem by calculating non-dimensionalized added mass and damping coefficients to the mass of the fluid displaced by the submerged horizontal cylinder in either layer of a three-layer fluid, which is still otherwise. Under the linear theory of water waves, we investigate the circular cylinder's hydrodynamic forces by its swaying and heaving motion. The time-harmonic wave propagates with three distinct wavenumbers for a given frequency. The method of multipoles has been employed due to its rapid converging solutions by increasing the truncation limit. Potential functions are expressed into systems of linear algebraic equations, which are solved numerically for two sets of unknowns in each case by truncation. Then, the added mass and damping coefficients are obtained from the non-dimensionalized hydrodynamic forces when the horizontal circular cylinder is submerged in the lower, middle and upper layers, respectively. The obtained results are depicted graphically against wavenumber in numerous figures and analysed.
A novel doubly salient single phase permanent magnet generator

This paper presents the principles of operation, design and transient analysis of a newly developed single phase doubly salient permanent magnet (DSPM) generator. Design equations, finite element, and dynamic analysis of the new generator are described. The single phase DSPM generator has an extremely simple structure with two permanent magnets and four poles wound equipped with armature windings. The rotor of the variable reluctance machine has six poles and is laminated iron without windings. Both stator and rotor have a salient pole structure. Hence, the machine has the inverse geometry of recently reported three phase DSPM motor topologies. <<ETX>>
Globalization of Holomorphic Actions on Principal Bundles

Suppose G is a Lie group acting as a group of holomorphic automorphisms on a holomorphic principal bundle P → X. We show that if there is a holomorphic action of the complexification GC of G on. X, this lifts to a holomorphic action of GC on the bundle P → X. Two applications are presented. We prove that given any connected homogeneous complex manifold G/H with more than one end, the complexification GC of G acts holomorphically and transitively on G/H. We also show that the ends of a homogeneous complex manifold G/H with more than two ends essentially come from a space of the form S/Γ, where Γ is a Zariski dense discrete subgroup of a semisimple complex Lie group S with S and Γ being explicitly constructed in terms of G and H.
Estimated topological surface for 3D reconstruction using variational scheme

Abstract In this paper, we propose a novel variational scheme for three-dimensional (3D) reconstruction from point cloud using estimated topological surface. The scheme using estimated topological surface recovers the underlying 3D shape by integrating an estimated surface topology gradient field. The estimated gradient is usually non-integrable due to the presence of noise and outliers in the estimation process and inherent ambiguities. The method represents a continuum of surface reconstruction solutions of a given non-integrable gradient field. For an N×N point cloud, the subspace of all integrable gradient fields is of dimension N2−1. It can be applied to derive a range of meaningful surface reconstructions from this high dimensional space. We show that by using a progression of spatially varying anisotropic weights, significant improvements in surface reconstruction from point cloud can be achieved. Simulated surfaces are experimentally studied, and the results validate that the proposed approaches improve the reconstruction. The proposed method improves the reconstructing results significantly for the simulated data.
Biogeography Based Optimized Hybrid Chebyshev FLANN for Fingerprint Classification

The process of arranging fingerprint images based on their characteristics and features is called fingerprint classification. This process of classification eases the matching process from a large database. The test fingerprint image is compared with the database with respect to the classification process. For such type of complex classification FLANN is used as the classifier where the Biogeography based optimizer (BBO) is used to optimize the different parameters of classifier. This hybrid model proves to be reliable and provides high accuracy.
Analytical solution for 1D advection-diffusion-adsorption transport of landfill contaminants through a soil layer with finite thickness

Performance of liner systems is very important to prevent groundwater contamination by landfills.In accordance with the case that groundwater drainage layers are built beneath the liner systems in sanitary landfills,a 1D analytical model for advection-diffusion-adsorption process of contaminants through a soil layer with finite thickness is proposed.Cauchy boundary is adopted to simulate the mass migration to a zero concentration circumstance.The results show that the analytical solution coincides well with that of a commercial numerical software.The analytical solution is reasonable and accurate.Parametric analyses show that adsorption,diffusion and advection all have great influence on the breakthrough curves.To improve the performance of clayey liners,the soil layer with high adsorption ability is suggested,and leachate head need to be strictly controlled.
Symmetry-Driven Unsupervised Abnormal Object Detection for Railway Inspection

Vision-based abnormal object detection in railway track inspection images is one of the critical tasks to ensure the safety of railway transportation. Even though many machine learning-based methods have been developed, these approaches heavily rely on anomaly supervisions and therefore cannot detect unknown anomaly classes. In order to tackle the problem, this article proposes a novel unsupervised method to detect abnormal objects, which does not require abnormal object training data. Specially, we find that a railway track image is almost symmetrical about the track central line, i.e., normal objects appear repeatedly and symmetrically, while abnormal ones are rare and also significantly different from the corresponding symmetric areas. Motivated by this observation, we propose to train a metric-learning-based deep model to learn the similarity between normal objects and the corresponding symmetrical areas. Then for each object proposal in one test image, we measure the distance between the proposal and the corresponding symmetrical regions, and determine whether it is an abnormal object based on the symmetrical metric. Extensive experiments on our collected dataset show that our proposed method achieves competitive performance compared with the state-of-the-art methods.
A Two-Stage Fast Beam Search for Multi-User Millimeter-Wave Massive MIMO Systems

Millimeter-wave systems deploy spatial beam search for initial access. A narrow beam search results in significant discovery delays whereas wide beams cause high user misdetection. In this letter, a novel two-stage fast beam search algorithm is proposed that utilizes a beamforming architecture with an appropriate antenna spacing for subarray design. The first stage employs analog beamforming to identify the coarse beam space. The second stage based on hybrid beamforming makes use of pattern multiplication for beam space refinement. Simulation results demonstrate that the proposed scheme achieves improved performance in comparison to existing techniques in terms of significantly reduced beam search latency with a marginal increase in user misdetection.
Comparison with U ≈ 50 µK of neon samples of different isotopic compositions

New comparisons are reported of the triple-point temperatures, Ttp, of seven neon samples with different 22Ne amount concentrations, 22x, in cryogenic cells sealed by INRIM, NMIJ, NPL and PTB. This work stems from a collaboration between those laboratories aiming at establishing the relationship between Ttp and 22x. Several problems remained unsolved in the published results. The new measurements supply more thermal data of lower uncertainty, to help solve some of these problems. The detailed results from 35 meltings are reported, characterized by improved uncertainty, and include an analysis of calorimetric, thermometric and sample-related effects. A broad range of collected and processed information is analysed in detail in order to confirm expanded uncertainty levels of the order of 30 µK for a single cell and 50 µK for a comparison of pairs of cells. The quality of the INRIM results is then discussed. In most cases the uncertainty budget for the INRIM measurements, including analysis of chemical impurities and the hydrostatic head correction, reached the targeted improved uncertainty. Finally, the results are discussed and compared with previous studies in support of the future assignment by the CCT of an accurate and reliable relationship Ttp versus 22x. The paper discusses various improvements regarding the influence of the thermal measurements on both the uncertainty of the relationship and the ability to recognize either possible isotopic fractionation during sample preparation or incorrect assignment of 22x values to some measured samples.
ShadowMover: Automatically Projecting Real Shadows onto Virtual Object

Inserting 3D virtual objects into real-world images has many applications in photo editing and augmented reality. One key issue to ensure the reality of the composite whole scene is to generate consistent shadows between virtual and real objects. However, it is challenging to synthesize visually realistic shadows for virtual and real objects without any explicit geometric information of the real scene or manual intervention, especially for the shadows on the virtual objects projected by real objects. In view of this challenge, we present, to our knowledge, the first end-to-end solution to fully automatically project real shadows onto virtual objects for outdoor scenes. In our method, we introduce the Shifted Shadow Map, a new shadow representation that encodes the binary mask of shifted real shadows after inserting virtual objects in an image. Based on the shifted shadow map, we propose a CNN-based shadow generation model named ShadowMover which first predicts the shifted shadow map for an input image and then automatically generates plausible shadows on any inserted virtual object. A large-scale dataset is constructed to train the model. Our ShadowMover is robust to various scene configurations without relying on any geometric information of the real scene and is free of manual intervention. Extensive experiments validate the effectiveness of our method.
Prevalence and Molecular Characterization of Bovine Parainfluenza Virus Type 3 in Cattle Herds in China

Simple Summary BPIV3 is a common pathogen causing respiratory disease in cattle and a significant contributing factor to BRDC; however, data on the prevalence and molecular features of BPIV3 are still scarce in China. The current study sought to investigate the prevalence and molecular features of BPIV3 in BRDC-affected beef cattle and yaks in China, and the findings suggest that BPIV3 genotype C strains, the dominant strains in China, have a broad geographical distribution. Phylogenetic analysis of the HN and genomic sequences reveals Chinese strains with some unique genetic characteristics, which enhances our knowledge of the prevalence and genetic variation in BPIV3. Abstract Bovine parainfluenza virus type 3 (BPIV3) is a common respiratory pathogen that causes respiratory illness in cattle and makes a major contribution to the bovine respiratory disease complex (BRDC); however, data on the prevalence and molecular features of BPIV3 are still scarce in China. To investigate the epidemiological characteristics of BPIV3 in China, between September 2020 and June 2022, 776 respiratory samples were received from 58 BRDC-affected farms located in 16 provinces and one municipality. Those were screened for BPIV3 using a reverse transcription insulated isothermal PCR (RT-iiPCR) assay. Meanwhile, the HN gene and complete genome sequence of strains from different provinces were amplified, sequenced, and analyzed. The tests showed that 18.17% (141/776) of samples tested were positive for BPIV3, which originated from 21 farms in 6 provinces. Moreover, 22 complete HN gene sequences and 9 nearly complete genome sequences were obtained from the positive samples. Phylogenetic analysis based on the HN gene and complete genome sequences revealed that the sequences were clustered in one large clade for all Chinese BPIV3 genotype C strains, while overseas strain sequences of BPIV3 genotype C clustered into other clades. Moving beyond the known complete genome sequences of BPIV3 in GenBank, a total of five unique amino acid mutations were found in N protein, F protein, and HN protein in Chinese BPIV3 genotype C strains. Taken together, this study reveals that BPIV3 genotype C strains, the dominant strains in China, have a broad geographical distribution and some unique genetic characteristics. These findings contribute to our understanding of the epidemiological characteristics and genetic evolution of BPIV3 in China.
Effect of Numerical Aperture on Light Injection from Sources at the Core/Cladding Interface of a Fiber Optic Fluorosensor

The fluorosensor discussed here is a fluorescent cladding optical fiber sensor. The sensitivity of such a sensor depends on the amount of light injected into the fiber core due to a fluorescent cladding. Conditions necessary for high injection, were experimentally determined for side excitation. Previous theoretical work has predicted that, for a side excited fiber, the refractive index difference between the core and cladding, ncore−nclad, must be maximized to achieve high sensitivity. This prediction has used the exact field solution of an optical fiber and was qualitatively verified in the present work. This shows that standard optical fibers used for communication, which are weakly guiding, are not suitable for fluorosensor applications. Another result shows that the light injection increases with the fiber diameter.
Machine learning-based probabilistic profitable model in algorithmic trading

Abstract. Machine learning models are nowadays becoming ubiquitous in algorithmic trading and investment management. These models are mostly used in the pre-trade analysis phase to determine the buy or sell decisions using various machine learning techniques. We aim to implement a machine learning-driven approach using various technical indicators to predict stock market prices and then accordingly make a decision about buying or selling. First, an effective trading strategy is discussed that selects the potentially profitable stocks, and then the technical indicators such as simple moving average (SMA), exponential moving average (EMA), relative strength index (RSI), and moving average convergence divergence (MACD) are calculated for those potentially profitable stocks. Then supervised machine learning algorithms such as multiple linear regression, support vector machine regression, and decision tree regression are applied, where the close price of the stock is predicted using technical indicators for the next day, and based on that buy or sell signals are generated. The model is then tested on 12 different SNP500 stocks, one for every month in 2018, with the mean squared error (MSE) varying between 30.33 and 48.16 and the root MSE varying between 5.51 and 6.93, where the error is calculated on the difference in the number of days when the stock price actually increases and the predicted number of days for various models.
Effects of the Visual Fidelity of Virtual Environments on Presence, Context-dependent Forgetting, and Source-monitoring Error

Advances in virtual reality technology have enabled the creation of virtual environments (VEs) with significantly high visual fidelity when compared to real environments (REs). In this study, we use a high-fidelity VE to examine two effects caused by alternating VE and RE experiences: “context-dependent forgetting” and “source-monitoring errors. ” The former effect is that memories learned in VEs are more easily recalled in VEs than in REs, whereas memories learned in REs are more easily recalled in REs than in VEs. The source-monitoring error is that memories learned in VEs are easily confused with those learned in REs, making discriminating the source of the memory difficult. We hypothesized that the visual fidelity of VEs is responsible for these effects and conducted an experiment using two types of VEs: a high-fidelity VE created using photogrammetry techniques and low-fidelity VE created with primitive shapes and materials. The results show that the high-fidelity VE significantly improved the sense of presence. However, the level of the visual fidelity of the VEs did not show any effect on context-dependent forgetting and source-monitoring errors. Notably, the null results of the context-dependent forgetting between the VE and RE were strongly supported by Bayesian analysis. Thus, we indicate that context-dependent forgetting does not necessarily occur, which will be helpful for VR-based education and training.
Long-distance remote laser-induced breakdown spectroscopy using filamentation in air

We demonstrate remote elemental analysis at distances up to 90m, using a laser-induced breakdown spectroscopy scheme based on filamentation induced by the nonlinear propagation of unfocused ultrashort laser pulses. A detailed signal analysis suggests that this technique, remote filament-induced breakdown spectroscopy, can be extended up to the kilometer range.
Getting Our Ontology Right: A Critique of Language and Culture in the Work of François Jullien

This article presents a cognitive anthropological critique of François Jullien’s approach to language and culture. Jullien approaches ‘culture’ as a coherent set of concepts across time and space, relying primarily on identifying Chinese (and Greek) thought with particular concepts expressed in language. This mischaracterizes human culture, which exists on the level of individual mental representations, and relies on a form of linguistic determinism which fails to stand in the face of psychological and anthropological evidence. This leads Jullien to claim an incredible degree of cultural (and ontological) divergence between the Chinese and Europeans. By accounting for the distribution and dynamism of mental representations, the degree to which thought is underdetermined by language, and above all the divergence of intuitive and reflective cognition on the individual level, we can arrive at an alternative, ontologically realistic account of cultural divergence.
Multisource Maximum Predictor Discrepancy for Unsupervised Domain Adaptation on Corn Yield Prediction

Recently, with the advent of satellite missions and artificial intelligence techniques, supervised machine learning (ML) methods have been more and more used for analyzing remote sensing (RS) observation data for crop yield prediction. However, due to the domain shift between heterogeneous regions, supervised ML models tend to have poor spatial transferability. As a result, models trained with labeled data from one spatial region (i.e., source domain) often lose their validity when directly applied to another region (i.e., target domain). To address this issue, we proposed a multisource maximum predictor discrepancy (MMPD) neural network that is an unsupervised domain adaptation (UDA) approach for corn yield prediction at the county level. The novelties of this study include that: 1) we proposed to maximize the discrepancy between two source-specific yield predictors and align source and target domains by considering crop yield response in the target domain and 2) we adopted the strategy of multisource UDA to avoid negative interference between labeled samples from different sources. Case studies in the U.S. corn belt and Argentina demonstrated that the proposed MMPD model had effectively reduced domain shifts and outperformed several other state-of-the-art deep learning (DL) and UDA methods.
A Novel Millimeter Wave Receiver Enhanced by a Metallic Dipole Antenna and an Electro-Optic Polymer Waveguide Ring Resonator

The ever-demanding requirement for higher data transmission rate has pushed the carrier frequency in wireless communications from centimeter to millimeter waves (MMW) or even terahertz band. Hybrid integration between the wireless and fiber-optic systems can then help realize a seamless signal transition between the access and backbone networks. In this work, we propose a novel MMW receiver with a simple structure consisting of an electro-optic polymer racetrack waveguide ring resonator (WRR) and a metal dipole antenna. Due to Pockels effect within the polymer waveguide, the presence of the MMW electric field can introduce minor refractive index changes in the waveguide, which can affect light phase and the transmission through the WRR. This effect can be employed to effectively detect the presence of MMW based on the optical method. The phase changes can be highly improved by the dipole antenna resonating at the MMW frequency in order to achieve an enhanced detection of the MMWs. As a result, a low-cost, compact and reliable MMW receiver can be realized, which provides an effective functional interface between MMW and optical fiber-optic systems.
Development and Validation of the Breastfeeding Literacy Assessment Instrument (BLAI) for Obstetric Women

Background: Despite international efforts to protect and promote exclusive breastfeeding (EBF) for infants up to six months of age, global rates of EBF continue to fall short of the targets proposed by the WHO for 2025. Previous studies have shown a relationship between the level of health literacy and the duration of EBF, although this relationship was not determinant, probably due to the use of a generic health literacy questionnaire. Therefore, this study aims to design and validate the first specific breastfeeding literacy instrument. Methods: A Breastfeeding Literacy instrument was developed. Content validation was carried out by a group of 10 experts in health literacy, breastfeeding or instrument validation, obtaining a Content Validity index in Scale (S-CVI/Ave) of 0.912. A multicentre cross-sectional study was carried out in three Spanish hospitals to determine the psychometric properties (construct validity and internal consistency). The questionnaire was administered to 204 women during the clinical puerperium. Results: The Kaiser-Meier-Oklin Test (KMO = 0.924) and Bartlett’s Test of Sphericity (X2 = 3119.861; p ≤ 0.001) confirmed the feasibility of the Exploratory Factor Analysis, which explained 60.54% of the variance with four factors. Conclusions: The Breastfeeding Literacy Assessment Instrument (BLAI) consisting of 26 items was validated.