Miracle-dz/UniFAR-FTUs huggingface dataset:
A Relaxation Method for Modeling Two-Phase Shallow Granular Flows

We present a relaxation approach for the numerical solution of a depth-averaged two-phase model describing the flow of a shallow layer of a mixture of solid granular material and fluid. A relaxation model is formulated by introducing auxiliary variables that replace the momenta in the spatial gradients of the original system. These new variables are governed by linear equations with coefficients that determine the eigenvalues of the relaxation model. The proposed relaxation strategy results in the definition of a particular approximate Riemann solver for the original model equations. Compared to a Roe-type Riemann solver that we have proposed in previous work, the new solver has the advantage of a certain degree of freedom in the specification of the wave speeds through the choice of the relaxation parameters. This flexibility can be exploited to obtain a more robust method than the Roe type one in the treatment of wet/dry fronts. Some numerical experiments are presented to show the effectiveness of the proposed approach.
First observations of an outbursting comet with the MUSE integral-field spectrograph

We present the first observations of outbursting comet C/2015 ER61 (PANSTARRS) with the Multi-Unit Spectroscopic Explorer (MUSE). The comet was observed on April 9, 2017, only five days after it underwent a significant outburst during which the total brightness increased by a factor of ~7.5. Based on the MUSE observations, we produce simultaneous maps of the CN, C2, NH2 comae, and the dust coma. In turn, we applied image enhancement techniques in order to reveal features in the coma. By comparing the coma morphology for the dust and CN, C2, and NH2, we investigate the release mechanism of those gas species in the coma of comet ER61. We present evidence that NH2 could be released by icy or organic-rich dust grains.
3-D Gaussian–Gabor Feature Extraction and Selection for Hyperspectral Imagery Classification

Hyperspectral remote sensing imagery provides valuable and rich information to distinguish the characteristics of materials. However, this advantage of hyperspectral imagery often encounters the problem of a limited amount of training samples, which is caused by the difficulty of manually labeling. Fortunately, the spatial distribution of surface objects can be integrated with the spectral signature to improve the discriminative ability. In this paper, a 3-D Gaussian–Gabor feature extraction and selection framework has been proposed for hyperspectral image classification. First, a bank of 3-D Gaussian–Gabor filters are convolved with the concatenated data of both extended multi-attribute profile (EMAP) features and raw hyperspectral data. Second, an improved fast density peak clustering (IFDPC) method is introduced to select the most representative features from each extracted 3-D Gaussian–Gabor feature cube. Finally, the retained features are combined together to accomplish the classification task. The proposed method is thus named as GG-IFDPC. Three real hyperspectral imagery data sets have been utilized, and the experiments demonstrate the advantages of the proposed GG-IFDPC approach over the compared ones.
Service Quality dan Implikasinya Terhadap Kepuasan Pelanggan

The high level of society mobility needs to be supported by adequate infrastructures. Sufficient and adequate toll road infrastructure is able to increase the acceleration of a country's economic and social development through the creation of effectiveness and resulting efficiency. Optimal service delivery can gain customer satisfaction. One approach to measure service quality that many used as a reference is SERVQUAL (Service Quality). There are five dimension on servqual model, that is reliability, assurance, tangible, emphaty and responsiveness. The purpose of this study is to determine the level of customer satisfaction in order to improve the quality of service and to know the service attributes that are prioritized to be maintained and improved by PT Jasa Marga (Persero) Branch Purbaleunyi. Data processing methods in this study using the validity, reliability, classical assumption test, and descriptive statistical analysis. While the data analysis methods used are simple linear regression analysis, correlation, coefficient of determination and quadrant analysis. The results of this study indicate that service quality variables have positive and significant impact on customer satisfaction at Purbaleunyi toll road.
Incorporation of Heliospheric Imagery Into the CME Analysis Tool for Improvement of CME Forecasting

Coronal mass ejections (CMEs) cause the largest geomagnetic disturbances at Earth, which impact satellites, wired communication systems, and power grids. The CME Analysis Tool (CAT) is used to determine a CME's initial longitude, latitude, angular width, and radial speed from coronagraph images. These are the initial conditions for the Wang‐Sheeley‐Arge Enlil solar wind model, along with the ambient solar wind properties derived from magnetograms. However, the coronagraph imagery is limited by field of view. We have incorporated heliospheric imagery (HI) from the Solar Terrestrial Relations Observatory into CAT to create the CME Analysis Tool with Heliospheric Imagery (CAT‐HI). These HI images have a larger field of view, allowing tracking of CMEs to greater distances from the Sun. We have compared the performances of CAT and CAT‐HI by examining the expected arrival times of CMEs at the L1 Lagrange point and found them to be consistent. However, CAT‐HI is advantageous because it could be used to prune ensemble forecasts and issue routine updates for CME arrival time forecasts. Finally, we discuss CAT‐HI in the context of an operational mission at the L4 or L5 Lagrange points.
Fleckmentation: rapid segmentation using repeated 2-means

A procedure for segmentation is introduced that uses the K-means algorithm to decompose an image by recursively applying the K-means with k = 2 . This 2-means decomposition procedure finds any region that corresponds to a scene part, but it is also prone to oversegmentation. The procedure has already been used in two tasks previously: a scene classification task and a foreground–background segregation task (skin lesion detection). Here, the authors explain its operation in more detail and analyse its lower performance. It is sufficient to apply the procedure with only three to four recursions, making the process of almost minimal complexity O ( n 5 ) in case of four recursions. It is thus suitable for large-image analysis and frame processing.
Probing superfluid He 4 with high-frequency nanomechanical resonators down to millikelvin temperatures

Superfluids, such as superfluid $^3\mathrm{He}$ and $^4\mathrm{He}$, exhibit a broad range of quantum phenomena and excitations which are unique to these systems. Nanoscale mechanical resonators are sensitive and versatile force detectors with the ability to operate over many orders of magnitude in damping. Using nanomechanical-doubly clamped beams of extremely high quality factors ($Q>10^6$), we probe superfluid $^4\mathrm{He}$ from the superfluid transition temperature down to $\mathrm{mK}$ temperatures at frequencies up to $11.6 \, \mathrm{MHz}$. Our studies show that nanobeam damping is dominated by hydrodynamic viscosity of the normal component of $^4\mathrm{He}$ above $1\,\mathrm{K}$. In the temperature range $0.3-0.8\,\mathrm{K}$, the ballistic quasiparticles (phonons and rotons) determine the beams' behavior. At lower temperatures, damping saturates and is determined either by magnetomotive losses or acoustic emission into helium. It is remarkable that all these distinct regimes can be extracted with just a single device, despite damping changing over six orders of magnitude.
Hypermedia to connect them all: Autonomous hypermedia agents and socio‐technical interactions

Current standardization efforts of the Web of Things provide a unique opportunity to integrate technologies from the research domain of multiagent systems and the human‐computer interaction field that could take us closer to creating intuitively usable distributed artificial intelligence. In this paper, we discuss what bridges are missing between these largely detached research communities: we propose to use the Hypermedia As The Engine Of Application State principle that is at the core of the Web architecture to underpin systems that integrate multiagent planning and acting with semantic technologies and with interoperable mixed reality interfaces. This should enable the creation of highly augmented environments in private as well as commercial and industrial environments where physical and digital things coexist and interact with one another.
Inviscid instability of a unidirectional flow sheared in two transverse directions

Linear inviscid stability of general unidirectional flows sheared in one transverse direction has long been investigated by numerous researchers using the Rayleigh equation. However, unlike the simple shear flow considered in this equation, most physically relevant unidirectional flows vary in two transverse directions. Here the inviscid instability of such flows is studied by the large-Reynolds-number limit asymptotic analysis. We derive an a priori necessary condition for the existence of a limiting neutral mode, and develop a new numerical method to accurately capture singular neutral modes.
Finite time sliding mode control for piezoelectric actuators / Ahmed Abood Faraq Al-Hadad

The piezoelectric actuator (PEA) is a device which performs very small displacements within the range of less than or equal to 100 μm. However, the PEAs suffer from the inherent non-linearity because of the hysteresis, creep and vibrations effects. These effects may cause a displacement error as high as 15% of the total range. This high error shows the necessity of a controller. Due to Sliding Mode Control (SMC) simple design steps, high robustness and low sensitivity to disturbances, it has been implemented to overcome and minimize the error. This research project has successfully accomplished the following three objectives. The first is to overcome the robustness and accuracy issue by applying the SMC method which is robust and accurate, using the mathematical model of the PEA which includes all the uncertainties and disturbances. The second is to apply the Terminal Sliding Mode Control (TSMC) concept to overcome the global infinite time stability to reach convergence or zero error in finite time. The third is to reach chattering free non-singular system. Chattering and singularity phenomena reduce the service lifetime of the PEA and create unwanted noise in the control input single. To reduce the chattering, sigmoid (sig) function has been used in the sliding function. This method is called continues terminal sliding mode. To overcome the singularity problem, the sliding function and the derivative of the sliding function does not result in terms with negative (fractional) powers which mean there will be no singularity. The chattering free nonsingular finite time system has been proved mathematically and in the simulation.
Dual-frequency piezoelectric micromachined ultrasonic transducers

A piezoelectric micromachined ultrasonic transducer (pMUT) operating at dual frequencies (3.75 MHz and 18 MHz) was designed to achieve an ultrasound-on-a-chip solution for next-generation biomedical applications. Optimal electrode configurations for the dual-frequency pMUT were analyzed using finite element methods. It was found that a configuration with two ring electrodes enabled dual-frequency actuations of the diaphragm of the pMUT. Simulations showed that the first two resonances of the diaphragm can be tuned independently, especially with regard to amplitude, by optimizing electrode parameters (e.g., position and dimension) and applied voltage. It was also found that optimized distribution of the R and Z components of the displacement field contributes to near ideal mode superposition in a single diaphragm. Simulations for dual-actuations with both inner and outer ring electrodes showed that the two resonant modes are superimposed without significant vibrational crosstalk and result in high-quality dual-frequency acoustic radiation in water. Unlike transducers using two or more single-frequency ultrasonic resonators to generate dual-frequency ultrasound, every element of dual-frequency pMUTs exhibits dual-resonant response simultaneously.
The Effect of Gender on Spatial Ability and Spatial Reasoning Among Students in Grades 2-8

The purpose of this study was to examine gender differences across three types of spatial ability; namely, spatial perception, spatial visualization, and mental rotation in conjunction with working memory. The study utilized a causal-comparative research design involving group comparisons. In this design researchers collect data about variables that they have conceptualized to be in a causal relationship to each other, but there is no intervention as in experimental research. Participants in this study included approximately 200 students in second through eighth grades at one public school and one public charter school, all located in the same school district/county. Spatial ability was measured by four categories of spatial relations tests based upon spatial cognition research proposing that spatial cognition is comprised of “three separable dimensions:” the Mental Folding Test for Children (spatial visualization), an adaptation of the Differential Aptitude Test: Space Relations (DAT: SR), Mental Rotation for Children, an adaptation of the Mental Rotations Test (MRT), Manikin Test (spatial orientation and transformation), and Mr. Peanut Task (visuo-spatial working memory). The resultant scores were used as measures of mathematical achievement and cognitive ability. Data were analyzed using MANOVA and ANOVA statistical analysis. Results suggested that mostly non-significant differences exist for spatial visualization abilities between males and females. The sole example of a significant difference between male and females was noted on the Mr. Peanut test in the fourth and fifth grades, accompanied with a partial Eta Squared (ղ) of . 10.
Using learning analytics to assess student engagement and academic outcomes in open access enabling programmes

ABSTRACT Curriculum design, teaching methods, assessments and range of academic support need to be inclusive in Open Access Enabling courses. The findings of this study confirm a correlation between student access to online learning materials and a positive impact on grades in science courses. More specifically, students who frequently use the online learning system to access materials have better assessment and exam results.
Compact Air-Filled Luneburg Lens Antennas Based on Almost-Parallel Plate Waveguide Loaded With Equal-Sized Metallic Posts

This paper presents a Luneburg lens and a Luneburg reflector lens along with the corresponding compact and cost-effective multibeam antennas in the Ka-band. The Luneburg lens is composed of an air-filled parallel plate waveguide (PPW) loaded with equal-sized metallic posts. The upper plate of the PPW is designed to a curved surface to meet the requirement of the equivalent refractive index profile, meanwhile providing a transition between the lens and the feed waveguide. Also, based on the law of reflection, a reflecting wall is introduced to the Luneburg lens to achieve a Luneburg reflector lens. By employing the WR28 rectangular waveguides as the feeders, the Luneburg lens/reflector lens antennas with single beam and multiple beams are designed and experimentally verified in the whole Ka-band. The multibeam Luneburg lens antenna show the beam scanning from −45° to +45° with 0.6 dB scan loss. The multibeam Luneburg reflector lens antenna show the beam scanning from 30° to 60° with 0.7 dB scan loss. Besides, the acceptable performances of aperture efficiency, impedance matching and multiport isolation are achieved. The results indicate that the works in this paper have application potential in the millimeter-wave (mmW) wireless communications.
Optimal quantum optical control of spin in diamond

The nitrogen-vacancy (NV) center spin represents an appealing candidate for quantum information processing. Besides the widely used microwave control, its coherent manipulation may also be achieved using laser as mediated by the excited energy levels. Nevertheless, the multiple levels of the excited state of NV center spin make the coherent transition process become complex and may affect the ﬁdelity of coherent manipulation. Here, we adopt the strategy of optimal quantum control to accelerate coherent state transfer in the ground state manifold of NV center spin using laser. The results demonstrate improved performance in both the speed and the ﬁdelity of coherent state transfer which will be useful for optical control of NV center spin in diamond.
0 60 50 54 v 3 2 6 N ov 2 00 6 Geographical networks evolving with an optimal policy

Yan-Bo Xie, Tao Zhou1,2,∗ Wen-Jie Bai, Guanrong Chen, Wei-Ke Xiao, and Bing-Hong Wang Department of Modern Physics and Nonlinear Science Center, University of Science and Technology of China, Hefei 230026, PR China Department of Electronic Engineering, City University of Hong Kong, Hong Kong SAR, PR China Department of Chemistry, University of Science and Technology of China, Hefei 230026, PR China Center for Astrophysics, University of Science and Technology of China, Hefei 230026, PR China (Dated: November 28, 2006)
The Magnetic Activity of Low-Mass Stars

The purpose of this research project is to study magnetically active low-mass stars using data obtained with the Kepler Space Telescope. A sample of low-mass stars was identified in the Kepler Objects of Interest (KOI) database by selecting stars with a temperature below 3,700 K. Data were obtained from the Mikulski Archive for Space Telescopes and analyzed using the MATLAB software. Light curves showing the stellar flux vs time were generated for approximately 20 stars, and features showing stellar flares, star spots and planetary transits were identified. We found a weak relationship between magnetic activity and stellar diameter.
Enhanced generative adversarial networks for fault diagnosis of rotating machinery with imbalanced data

Deep learning-based methods have attracted the attention of researchers due to their outstanding performance in automatic feature learning, a crucial step for satisfactory fault diagnosis. However, faults in rotating machinery may occur occasionally, and fault-related signals are difficult to collect, resulting in imbalanced data. This problem is a major concern in fault diagnosis research. In this study, an enhanced generative adversarial network (EGAN) is proposed to establish a fault diagnosis model for rotating machinery. The model, based on a 2D convolutional network, consists of a generator and a discriminator. The generator produces specified samples to automatically enrich small samples for balancing datasets. The discriminator validates the distribution similarity between the generated and original samples. Fault types are recognized by a classifier using the generated and original samples. An adaptive training ratio strategy is also proposed to improve the convergence rapidity and stability of the EGAN training process. In case studies, two datasets are applied to verify the generalization performance of the EGAN. Results confirm the superior performance of the EGAN in fault diagnosis, particularly in the analyses of datasets with imbalanced data. Moreover, visualization results demonstrate that the proposed EGAN exhibits satisfactory ability and great potential for fault diagnosis applications of rotating machinery.
A memristor-based system with hidden hyperchaotic attractors, its circuit design, synchronisation via integral sliding mode control and an application to voice encryption

A memristor-based system with hyperchaos and hidden attractors is introduced in this research work. The proposed four-dimensional memristor-based system exhibits both line equilibrium and no-equilibrium for different choice of parameters. An experimental emulation of the memristor-based system is carried out by an electronic circuit. An adaptive integral sliding mode controller is designed for globally synchronising a pair of memristor-based hyperchaotic systems with unknown parameters. As another application, the memristive system with hyperchaos is applied for voice encryption, which has potential applications in cryptosystems, computing and secure communication.
Data quality measures for identity resolution

The explosion in popularity of online social networks has led to increased interest in identity resolution from security practitioners. Being able to connect together the multiple online accounts of a user can be of use in verifying identity attributes and in tracking the activity of malicious users. At the same time, privacy researchers are exploring the same phenomenon with interest in identifying privacy risks caused by re-identification attacks. Existing literature has explored how particular components of an online identity may be used to connect profiles, but few if any studies have attempted to assess the comparative value of information attributes. In addition, few of the methods being reported are easily comparable, due to difficulties with obtaining and sharing ground- truth data. Attempts to gain a comprehensive understanding of the identifiability of profile attributes are hindered by these issues. With a focus on overcoming these hurdles to effective research, this thesis first develops a methodology for sampling ground-truth data from online social networks. Building on this with reference to both existing literature and samples of real profile data, this thesis describes and grounds a comprehensive matching schema of profile attributes. The work then defines data quality measures which are important for identity resolution, and measures the availability, consistency and uniqueness of the schema’s contents. The developed measurements are then applied in a feature selection scheme to reduce the impact of missing data issues common in identity resolution. Finally, this thesis addresses the purposes to which identity resolution may be applied, defining the further application-oriented data quality measurements of novelty, veracity and relevance, and demonstrating their calculation and application for a particular use case: evaluating the social engineering vulnerability of an organisation.