Miracle-dz/UniFAR-FTUs huggingface dataset:
A Level Set Immersed Boundary Method for Water Entry and Exit

The interaction between free surface flow and structure is investigated using a new level set immersed boundary method. The incorporation of an improved immersed boundary method with a free surface capture scheme implemented in a Navier-Stokes solver allows the interaction between fluid flow with free surface and moving body/bodies of almost arbitrary shape to be modelled. A new algorithm is proposed to locate exact forcing points near solid boundaries, which provides an accurate numerical solution. The discretized linear system of the Poisson pressure equation is solved using the Generalized Minimum Residual (GMRES) method with incomplete LU preconditioning. Uniform flow past a cylinder at Reynolds number Re=100 is modelled using the present model and results agree well with the experiment and numerical data in the literature. Water exit and entry of a cylinder at the prescribed velocity is also investigated. The predicted slamming coefficient is in good agreement with experimental data and previous numerical simulations using a ComFlow model. The vertical slamming force and pressure distribution for the free falling wedge is also studied by the presentmodel and comparisons with available theoretical solutions and experimental data are made. AMS subject classifications: 6D05, 76T10, 65B99, 65E05
B0218+35.7 - A GRAVITATIONALLY LENSED SYSTEM WITH THE SMALLEST SEPARATION

Using data from the VLA, the newly enhanced MERLIN and the European VLBI Network, we have established that the strong flat-spectrum radio source B0218 + 35.7 is a gravitationally lensed system. The overall image separation of 335 milliarcsec (mas) is one-third that of any other securely established lensed system. The structure of B0218 + 35.7 is simple. It has two bright flat-spectrum compact components, one of which lies inside and the other outside a steep-spectrum «Einstein ring» whose diameter is the same as the separation of the bright components. The spectral and polarization properties of these bright components, together with their substructure revealed by European VLBI, leave no doubt that they are two images of a single source
MOLECULAR GAS DISK STRUCTURES AROUND ACTIVE GALACTIC NUCLEI

We present new high-resolution numerical simulations of the interstellar medium (ISM) in a central R ⩽ 32 parsecs region around a supermassive black hole (1.3 × 107 M☉) at a galactic center. Three-dimensional hydrodynamic modeling of the ISM (Wada & Norman 2002) with the nuclear starburst now includes tracking of the formation of molecular hydrogen (H2) out of the neutral hydrogen phase as a function of the evolving ambient ISM conditions with a finer spatial resolution (0.125 pc). In a quasi-equilibrium state, mass fraction of H2 is about 0.4 (total H2 mass is ≃1.5 × 106 M☉) of the total gas mass for the uniform far ultra-violet (FUV) with G0 = 10 in Habing unit. As shown in the previous model, the gas forms an inhomogeneous disk, whose scale height becomes larger in the outer region. H2 forms a thin nuclear disk in the inner ≃5 pc, which is surrounded by molecular clouds swelled up toward h ≲ 10 pc. The velocity field of the disk is highly turbulent in the torus region, whose velocity dispersion is ≃20 km s−1 on average. Average supernova (SN) rate of ≃5 × 10−5 yr−1 is large enough to energize these structures. Gas column densities toward the nucleus larger than 1022 cm−2 are observed if the viewing angle is smaller than θv ≃ 50° from the edge-on. However, the column densities are distributed over almost two orders of magnitude around the average for any given viewing angle due to the clumpy nature of the torus. For a stronger FUV (G0 = 100), the total H2 mass in an equilibrium is only slightly smaller (≃0.35), a testimony to the strong self-shielding nature of H2, and the molecular gas is somewhat more concentrated in a midplane. Other properties of the ISM are not very sensitive either to the FUV intensity or the SN rate. Finally, the morphology and kinematics of the circum nuclear molecular gas disks emerging from our models are similar to that revealed by recent near infrared observations using VLTI/Keck.
Ferroelectric Phase Transitions and Electroconducting Properties of Ceramic BIMEVOX Solid Solutions (Me = La, Zr)

Ceramic solid solutions Bi 4 (V 1−x Zr x ) 2 O 11−z (I), (Bi 1−y La y ) 4 V 2 O 11−z (II) and (Bi 1−y La y ) 4 (V 0.96 Zr 0.05 ) 2 O 11−z (III) with x < 0.30, y < 0.20, were prepared by the solid state reaction method and were investigated by means of various experimental techniques. The low temperature ferroelectric α-phase exists in the solid solutions with x, y ≤ 0.05. The phase transition to paraelectric β-phase was revealed in these compositions by the dielectric spectroscopy, SHG and DTA/DSC methods. Dielectric permittivity measurements confirmed an effect of the domain walls “pinning” due to the presence of oxygen vacancies inherent to the bismuth vanadate based structures. Switching of ferroelectric domains by an external electric field was observed in Piezoresponse Force Microscopy experiments.
CMOS Reference Voltage Source Using Drain Current Temperature Characteristics

This paper proposes a CMOS reference voltage source that is insensitive to changes in power supply and temperature, targeted for consumer electronics applications where low cost is mandatory. The proposed reference voltage source consists of diode-connected MOSFETs in series and the bias current generator with the Nagata current source insensitive to temperature as well as supply voltage. This circuit utilizes the drain current temperature characteristics for the temperature insensitivity realization. The basic circuit design idea, circuit topology and SPICE simulation results with TSMC $0.18\mu \mathrm{m}$ CMOS parameters are shown. Also, its application to the reference voltage distribution on the entire chip is discussed.
Explainable Artificial Intelligence for Smart Grid Intrusion Detection Systems

A popular approach to overcome the complexity of cybersecurity and sophistication of cyber attacks is implementing artificial intelligence (AI)-based security controls that integrate machine learning (ML) algorithms into security controls, such as intrusion and malware detection. These AI-based security controls are considered more effective than traditional signature-based and heuristics-based controls. However, the growing adoption of advanced ML algorithms is turning these AI-based security controls into black-box systems. We postulate that these black-box AI methods would make risk management and informed decision-making challenging. Using smart grid intrusion detection as our context, we illustrate our arguments by outlining a risk assessment plan to discuss the transparency and interpretability of an AI-based security control. We contribute to the literature by changing the focus from performance to explainability of algorithms, highlighting critical steps in explainability for integrating into risk assessment planning, and outlining the implications of explainability in AI-based intrusion detection systems.
Exploring programming didactics in primary school - a gender perspective

This Research Full Paper explore inclusion in programming in primary school. Education plays a crucial role in engaging a diverse group of students with different social backgrounds and interests. Therefore, this study aims to shed light upon inclusion in programming in primary school, focusing on gender to increase the knowledge regarding inclusion in programming didactics. The following research questions have guided the study: How are programming activities designed in primary school? How do pupils approach the programming tasks given? Can any gender differences be observed, and what are the consequences for the teaching practice? The theoretical framework used to analyse the empirical material is at the intersection between multimodal social semiotics [1] and a design-oriented perspective [2]. The empirical material consists of classroom video observations. Programming lessons in grades 4-8 have been observed and videos was recorded during 2019-2020. The pupils have worked on eight different programming tasks during the lessons. Analysis of these programming activities (tasks, instructions and resources used) focusing on gender has been made. Findings show two aspects 1) interest and position and 2) representations of knowledge. Regarding interest and position, the study of programming activities shows both similarities and differences between girls’ and boys’ approach to the task. Similarities are shown regarding the learning activities. No differences in coding strategies or creativity are observed if the task has an open design. The differences are shown in the guided tasks, where boys tend to engage in the tasks from their interests rather than following instructions and girls tend to follow the instructions given by the teacher. From a gender perspective, the boys might find programming more creative and fun, and the girls might feel less engaged as their interest falls into the background. Secondly, knowledge representations might affect who is seen as an expert within the CS field. For example, in grades 4 and 5, a male voice was represented in the video clips and a guest teacher used when presenting programming activities. The resources used in the lessons can be seen as representations of knowledge. In this case, they are always connected to a social and cultural domain [3], an environment foremost represented by males in this case.
2DoF BTSPWM for Parallel Current Source Converter with Improved CMV and Harmonic Performance

The three-phase current source converter (CSC) can work as a buck-type rectifier or boost-type inverter with a wide voltage regulation range, which is a promising solution for single-stage power conversion. Parallel connection of power converters is a popular choice to increase the power rating and system reliability. Meanwhile, supervisor harmonic performance can be achieved through interleaving operation. Carrier-based sinusoidal pulse width modulation (SPWM) is widely adopted for interleaved converters due to its easy and modular implementation features. In this paper, a 2 degree-of-freedom (2DoF) optimization-based bi-tri logic SPWM (BTSPWM) is proposed for a parallel CSC system, where the harmonic spectrum of output and common-mode voltage (CMV) is analyzed comprehensively. The carrier shift angles are optimized to improve the CMV and harmonic performance for interleaving BTSPWMs. Meanwhile, the optimized zero-state replacement (ZSR) method can be combined with 2DoF modulations to further reduce the CMV. The effectiveness of the proposed method is verified on a 2-CSC parallel system with experiment results.
Star distribution around a massive black hole in a globular cluster. II. Unequal star masses

The steady-state distribution of stars around a massive black hole in a globular cluster is determined by solving numerically the coupled time-dependent Boltzmann equations for a system containing stars of two different masses. Similar results are found for an arbitrary spectrum of masses with the aid of approximate analytic solutions of the time-independent equations. The effects of mass segregation are summarized by scaling laws that are derived both by analytic approximations and by numerical solutions. The detectability of a black hole in a globular cluster is discussed in terms of possible observations of the central star distributions.
Training Spiking Autoencoders by Truncated BPTT under Trade-offs between Simulation Steps and Reconstruction Error

This article presents a comprehensive study of trade-offs between simulation steps and reconstruction performance for spiking autoencoders. We execute training and inference of a spiking neural network to reconstruct FashionMNSIT dataset images for several simulation step configurations and evaluate reconstruction accuracies by mean squared error. Experiments showed that a longer simulation step configuration indeed improves reconstruction accuracy while the improvement gets a peek at a very long configuration. Flexible design on the training configuration will be applicable; for example, shorter steps could be acceptable for accurate-insensitive but latency-restricted systems.
Fault Diagnosis using Shallow Neural Networks for Voltage Source Inverters in SynRM Drives

This paper presents a neural-based fault diagnosis system for a two-level Voltage Source Inverter that is used to drive the Synchronous Reluctance Motors. In particular, three classes are considered: Healthy, Open Circuit Fault (OSF) and Short Circuit Fault (SCF). The proposed strategy relies on the data generated by the mathematical models of OSF and SCF together with the healthy configuration. For each category of fault, multi-switch faults have been emulated. Following the acquisition of healthy and faulty three-phase currents, exploratory analysis of the data is conducted using Principal Component Analysis. Thereafter, various Machine Learning techniques have been utilized to develop different types of classifiers. The best classification model after considering factors like time complexity, storage, confidence level of outputs was the shallow Long-Short-Term-Memory neural network.
On existence and concentration behavior of ground state solutions for a class of problems with critical growth

In this paper, we study the existence and the concentration behavior of ground state for the problem $-h^2\Delta u+V(z)u=\lambda u^q+u^{2^{ *} -1,\mathbb R^N $ $u(z)>0\quad $ for all $z\in \mathbb R^N \qquad\qquad\qquad\qquad\qquad\qquad\qquad (P_{h})$ where $h, \lambda >0$, 1<$q$ <$2^{ * -1$ $=\frac{N+2}{N-2}$, $N\geq 3$ and $V: \mathbb R^N\to \mathbb R$ is a positive function such that 0< $i nf_{z\in\mathbb R^N}V(z)$< $limi nf_{|z| \rightarrow \infty}V(z)=V_{\infty}. $
MultiPath ViT OCR: A Lightweight Visual Transformer-based License Plate Optical Character Recognition

Because of the natural conditions of license plate images, the Optical Character Recognition (OCR) of these images is generally a challenging problem. OCR systems are utilized in edge devices with limited computation power. Despite the considerable progress of deep neural networks, state-of-the-art models are not always an excellent solution to this problem. Most models have many parameters, and in practice, they need many resources to train, maintain and implement on edge devices. We propose a lightweight model based on Visual Transformer architecture and achieve competitive results against traditional CRNN models. Due to the lack of a rich and large-scale dataset for Persian license plates, we gathered and annotated 1.3M images of license plates in various natural conditions from different points of view and different cameras. We call this dataset as LicenseNet. Our proposed model achieves 77.25% accuracy against CNN models with 75.18% accuracy and embedded OCR models in cameras with 60.37% accuracy on the LicenseNet test set. Furthermore, we achieved better accuracy with 3.21 times fewer training parameters than previously proposed models.
Seeking the simultaneously socially committed, successful, and employable engineering student

In this work in progress research paper, we ask what is required of a 21st century engineer. The study focuses on a new program of cross-disciplinary courses set to prepare engineering students for work-life and societal challenges, as well as addressing diversity among engineering students. We study the information material used in recruiting students to the program, which is organized in parallel to the students’ ordinary engineering programs. Using a discourse analytical approach, by analyzing the rationales used for urging students to join, and what broader interpretative repertoires these rationales draw from, we aim to highlight what is framed as important and successful for today’s engineering students. Preliminary results show how prospective students are described as simultaneously especially employable and especially committed, for example to solving real problems related to climate change. The program is marketed to students in ways that draws on repertoires of progressivism, neo-liberalism, and sustainability, as well as the importance of collaboration and teamwork for employability. In earlier research, these kinds of repertoires have been shown to both open up and limit student possibilities to identify as authentic engineers along intersecting lines of gender, social class, age and ethnicity. Importantly, some of them function as fluid boundary repertoires which allows contradictory values to be put together in the picture of the successful contemporary engineering student.
EXTRATO DE ALHO NA SUPERAÇÃO DA DORMÊNCIA DE PEREIRA ‘HOUSUI’

In order to evaluate the effect of garlic extract to induce break dormancy in pear trees under low chilling conditions an experiment was installed in a 7-year-old ‘Housui’ pear commercial orchard located in Campo Largo, PR, presenting tree spacing with 1,5 m between plants x 4,0 m between rows. The treatments were applied when plants were in the B (swollen bud) phenological stage which happened on 08/17/2008. A completely random experimental design of 6 treatments with 3 replications was used. Treatments were: 1) Control; 2) 4% Mineral Oil (MO); 3) 0.52% hydrogen cyanamide (HC) + 4% MO; 4) 1% Garlic Extract (GE) + 4% MO; 5) 2.5% GE + 4% MO; and 6) 5% GE + 4% MO. The evaluations were performed 29, 36, 43, 50 and 57 days after product application. Garlic extract exerted influence on break dormancy of the pear buds. Treatment with 5% GE + 4% MO anticipated bud burst, presented similar results to those obtained with 0.52% HC + 4% of MO. No treatment was toxic to the plants.
Design of A Bipedal Robot via Modular Actuator

Bipedal robot is a main part of humanoid robot. In general, the greater the number of degrees of freedom of a robot, the more sophisticated electro-mechanical design and the more adaptable ability of a robot. There are many ways to construct a bipedal robot. One of them is to construct it directly with several modular actuators. The advantage of modular design is that it can reduce the complexity of the system. In this paper, a bipedal robot that consists of 12 modular actuators is introduced. Firstly, a modular actuator with a double support structure and a bipedal robot constructed by these modular actuators are introduced. Then, gait planning and simulation of this robot based on zero moment point principle is introduced. Finally, the prototype of this robot is presented and discussed.
Spaced Retrieval Practice in Two Fundamental Engineering Courses: Calculus and Physics

This full-length, research-to-practice paper discusses an ongoing NSF project aimed at implementing spaced retrieval practice in Science, Technology, Engineering, and Mathematics (STEM) classes. By implementing evidence-based learning practices in the classroom, educational performance, and thereby retention and graduation rates, are thought to be improved. Retrieval practice refers to the repeated recall of information from memory to increase the information’s future accessibility. Retrieval practice can be massed or spaced. In massed practice, all acts of recall occur during a single short temporal window. In spaced practice, acts of recall are temporally distributed. This project implements spaced retrieval practice in STEM barrier courses in the fields of biology, chemistry, engineering, mathematics, physics, and psychology. The current dissemination focuses on spaced retrieval practice in one section of Calculus for Engineers and two sections of Fundamentals of Physics I, with similarly delivered implementations. Spaced retrieval practice significantly improved student performance in calculus but not in the two sections of physics. Potential explanations for these results are discussed, as well as implications.
SARS-CoV-2 Detection Using Chest X-Ray Images with Deep Learning Methods

With the coronavirus invading the world from China since December 2019, it has led every country into crisis. The World Health Organization (WHO) declared the coronavirus a pandemic on March 11, 2020. The search for solutions began all over the world. One of the solutions is applying artificial neural networks for classification methods on chest X-ray images. Chest X-ray (CXR) scan images can be considered a confirmatory approach as they are quick to obtain and easily accessible. When these images are used, transfer learning from deep learning methods is the most preferred method to detect infected patients. Three different datasets, varying in different sample sizes, were used for training our models and further detailed analysis. The outputs of the results are measured by looking at the F1 score and accuracy. With the comparative performance analysis, it was seen that the InceptionV3 and Xception models had the highest overall accuracy and F1 scores than the other models for our datasets.
Fixed-Time Adaptive Fuzzy Containment Dynamic Surface Control for Nonlinear Multiagent Systems

This article investigates the fixed-time adaptive fuzzy containment control problem for nonlinear multiagent systems under the directed communication topologies. The controlled systems have the unknown internal dynamics and mismatched disturbances, and fuzzy logic systems are utilized to identify the unknown internal dynamics. The mismatched disturbances and approximate errors are reconstructed via a disturbance observer. Then, by introducing an adding power integral method, a fixed-time adaptive fuzzy containment DSC scheme is developed to deal with the problem of “computation complexity. ” The presented containment control method can not only guarantee that the controlled system is semiglobal practical fixed-time stable, but also avoid the “singular problem” in fixed-time backstepping recursive control technology. Finally, an application of marine surface vehicle is provided to verify the effectiveness of the presented fixed-time fuzzy containment control method.
Implementation and Comparison of U-net networks for Automatic COVID-19 Lung Infection Segmentation

With the big number of COVID-19 patients, efficient detection tools are necessary. In this work, we proposed an automatic detection and quantification tool based on deep learning model. The architecture used is U-Net architecture, one of the most known for medical applications. This network was introduced as a binary semantic segmentation tool. It uses a dataset of 100 images, seventy-two of them for training, ten for validation, and eighteen for testing. The model will be compared with other deep learning models and evaluated using several evaluation metrics. The results have shown an accuracy of 0.958, sensitivity of 0.900, dice coefficient of 0.726, and a specificity of 0.962. Compared with other related works, our network has the best accuracy and specificity. The obtained results show the ability of the model as a binary segmentation tool to separate infection tissue and healthy tissue.