Miracle-dz/UniFAR-FTUs huggingface dataset:
Iron, Cobalt, and Nickel in SN 1987A

During the first several weeks after the explosion of SN 1987A, Fe/Co/Ni clumps, containing approx. 1% of the mass of the supernova envelope, absorbed most of the energy release by Ni-56 and Co-56 decay. As a result, the clumps expanded relative to the substrate, forming a 'nickel bubble' of low-density Fe/Co/Ni. Later the clumps captured approx. 10% of the radioactive luminosity of gamma rays and positrons. Assuming that these elements are not mixed microscopically with other elements, we find that the clumps must occupy approx. greater than 30% of the volume of the emitting region (radial velocity approx. less than 2500 km/s). The result indicates that the emission at late times is dominated by an extra source of heating and ionization, most likely photoionization by two-photon continuum from metastable helium in the gas surrounding the clumps. The resulting 'frothy' structure, consisting of bubbles of low-density Fe surrounded by higher-density filaments of H, He, and other elements, will persist and may be seen in the spectra and structure of supernova remnants.
Subpopulation Analysis in Causal Inference: A Healthcare Case Study

Treatment interventions are usually targeted to improve a specific outcome on as elected group of patients who are eligible to receive the treatment. The success of such treatments is determined by the post-intervention treatment effect on the population under consideration. There are cases when the treatment group contains multiple categories of eligible populations, with various effects, especially when the study’s criteria are loosely defined. I n such s tudies (non-targeted trials) non-eligible subjects may be treated, producing heterogeneous treatment effects within the treated group. Inferring the effectiveness of the treatment under this scenario is difficult since the average treatment effect on the treated is a combination of multiple effect levels. This can bias the resulting conclusion of the causal studies. We propose an end-to-end framework based on matching and unsupervised clustering for extracting population sub-groups based on their effect levels. We demonstrate our methods on a real-world healthcare application, highlighting the value of subpopulation analysis for recovering multiple effect groups.
Tracing Randomly Oriented Filaments in a Simulated Actin Network Tomogram

The disordered nature of the actin network in Dictyostelium discoideum filopodia makes identifying filaments within noisy cryo-electron tomograms extremely challenging. In this work, we present a computationally efficient dynamic programming-based framework for tracing arbitrarily oriented actin filaments. Starting from locally determined seed points, it accumulates densities along paths of a particular length within 45° of the three Cartesian coordinate axes. This novel approach covers all possible orientations, so there is no need to assume a dominant direction as in earlier work. For each seed point, the path with the highest density value is selected, and it acts as a candidate filament segment (CFS) that is likely to form a part of a filament when it has a high path density value. The subsequent stages involve identifying groups of CFSs with high path densities by binning and merging them. The merging step considers the relative orientations and distances of CFSs to connect them. In addition, the CFSs are extended to fill the noise-induced gaps to some extent. In the current prototype software, we focused on the proof of the concept, using a noisy simulated tomogram with a known ground truth that closely mimics the appearance of an experimental map. We achieved an almost perfect precision score of 0.999, but this success came at the expense of a lower recall score 0.462 due to false negatives. We discuss the dependencies as well as the limitations of the current filament merging that need to be overcome to achieve a higher recall score in the future.
Dual-OAM-mode Hexagonal Microstrip Ring Antenna Design

This paper presents the design of a microstrip ring antenna able to generate a dual-mode orbital angular momentum (OAM) mode. The patch consists of a hexagonal shape with a hexagonal slot inside it since the hexagonal structure of the antenna is simple, easy to design, and compatible in size. Two SMA connectors have been placed and excited properly to generate the required modes. The antenna operates at 3.515 GHz and generates OAM modes of (l = ±1). Simulation results show that the dual modes have good vortex phase characteristics. Also, the single-element antenna has a gain of 3.5 dB and a bandwidth of 110 MHz. This antenna has potential in the 5G field applications.
Design and Implementation of Three-Phase Inverter using MOPSO

In this paper, the design and stability of three-phase inverter is tested experimentally and evaluated using simulation. The voltage control loop is implemented in the dq reference frame which contains two proportional- integral (PI) controller. The PI controller is used to regulate the voltage of the three-phase inverter. Arduino is used to implement the PI controller of voltage control loop. Multi objective Practical Swarm optimization (MOPSO) is used to derive the optimum value of the PI controller gains which are then tested using simulation. The simulation and practical results show the stable operation of the voltage of the three-phase inverter.
Frequency-based Directional Sensing for Mobile Molecular Communication

This paper proposes a mobile molecular communication system in which directional information is encoded on pulse frequency. In the proposed system, a sender bio-nanomachine generates pulses of signal molecules periodically, the pulses of signal molecules propagate in the environment, and the receiver bio-nanomachine learns the direction to the sender bio-nanomachine from the frequency of pulses that it observes. This paper presents its model and conducts computer simulations. Results show that receiver bio-nanomachines using frequency-based directional sensing are capable of moving closer to the sender bio-nanomachine successfully. Results also show that the performance of the frequency-based directional sensing scheme depends on the noise strength and that, as the noise strength increases, the mean time to reach the sender and its variance increase. The paper proposes a new frequency-based modulation scheme for mobile molecular communication and contributes to the field of molecular communication.
Contour Offset Map: A New Component Designed for Smooth and Robust Optic Disc/Cup Contour Detection

The optic disc and cup contour detection task has been studied extensively, as it is the predominant attribute to calculate cup-to-disc ratio (CDR) and further diagnose glaucoma. Currently, methods for optic disc and cup contour detection task mostly include two types: the coordinate point regression and the probability map classification. Normally, the latter is better. However, the probability map is unfavorable to be directly applied to the optic disc and cup contour detection task, which makes the model outputs more than one optic disc and cup in some cases, and uneven results. In addition, the probability map classification methods can not directly optimize the calculation of CDR in the training process, which further limits the performance of neural networks. In this paper, we design a new optic disc and cup contour representation method called Contour Offset Map to solve the two mentioned problems. Our approach can be applied seamlessly to the existing semantic segmentation networks by only modifying the loss functions and the number of output maps rather than the network structure. Moreover, a rim loss and a CDR loss are introduced to further improve the performance of our method in the training process. Experiments on our private dataset and a public dataset demonstrate the superior performance of our Contour Offset Map on the task of the optic disc and cup contour detection.
Self-efficacy in engineering design through peer review, self-review, and interactive online tutorials

Self-regulated learning is a key attribute in tertiary engineering education, and forms the basis of engineering judgement. The experience of remote learning during the COVID-19 era revealed particular challenges in self-regulated student learning practices, but also resulted in a number of systemic, technology-based interventions to enable improved student learning. Drawing on a 3rd-year electronic design course case study at a contact-based engineering faculty in South Africa, this paper presents an approach to bridging the gap between student perceptions and their actual assessment performance during independent, remote learning. Using scaffolded reflective and peer learning strategies, the research team sought to answer the question: What is the impact on self-efficacy of frequent self- and peer-assessment opportunities across a range of project-based learning tasks? Results were analysed using Bandura's four self-efficacy 'mastery’ and experiential domains, and indicate an improvement in alignment between perceptions and actual performance. We suggest that a well-designed, scaffolded set of assignments with reflective and peer-learning opportunities can contribute significantly to the development of student confidence and mastery.
Comprehensive analysis of gene expression profiles to radiation exposure reveals molecular signatures of low-dose radiation response

There are various sources of ionizing radiation exposure, where medical exposure for radiation therapy or diagnosis is the most common human-made source. Understanding how gene expression is modulated after ionizing radiation exposure and investigating the presence of any dose-dependent gene expression patterns have broad implications for health risks from radiotherapy, medical radiation diagnostic procedures, as well as other environmental exposure. In this paper, we perform a comprehensive pathway-based analysis of gene expression profiles in response to low-dose radiation exposure, in order to examine the potential mechanism of gene regulation underlying such responses. To accomplish this goal, we employ a statistical framework to determine whether a specific group of genes belonging to a known pathway display coordinated expression patterns that are modulated in a manner consistent with the radiation level. Findings in our study suggest that there exist complex yet consistent signatures that reflect the molecular response to radiation exposure, which differ between low-dose and high-dose radiation.
Aggregate message authentication codes for DAG structures

A message authentication code (MAC) scheme is a means for verification of a message in a symmetric key setting. An aggregate MAC (AMAC) scheme is proposed for decreasing the communication cost (tag size) generated by plural tag generators. Up to present, we can see an AMAC scheme for parallel construction, one for sequential construction, and one for series-parallel construction. In this paper, we present how to construct and implement an AMAC scheme in which a tag generation order represented by a general DAG is available, and discuss the security and the tag size for the scheme. The first implementation gives a simple HMAC-like AMAC scheme, but the security of such a scheme is shown only under a weak security model. The second one gives an AMAC scheme which is shown to be secure under a strong security model, but the tag size increases in proportion to the number of the paths from the sources to the sinks. Then we discuss also how to decrease the tag size.
Predicting the functional effects of human non-coding variants based on stacking ensemble learning

Predicting the functional impact of genetic variants in non-coding regions of the human genome can aid in the elucidation of the etiology of diseases or traits. In recent years, an increasing number of methods to predict the impact of sequence variation in non-coding regions of the human genome have been developed. However, most of current studies are limited to predict specific types of non-coding variants. To address this problem, here we propose a non-coding SNVs prediction method based on stacking integration strategy. The method consists of three stacking models built using the same strategy based on different causality assumptions to predict functional, pathogenic, and cancer driver non-coding SNVs, respectively. We demonstrate that our method outperforms the other seven methods. In addition, a comparison of our proposed model with other methods for non-coding de novo mutations in autism spectrum disease reveals that our model has the highest discriminative ability, indicating that its performance is stable and superior in different scenarios.
Smart Predictive Maintenance Enabled by Digital Twins and Smart Big Data: A New Framework

Complexity and performance requirements of the control systems are increasing dramatically along with fault diagnosis and predictive maintenance as transformation of industry 4.0 continues. Hence, both literature and industry requires a comprehensive and effective predictive maintenance and health monitoring tools. There are many different wellestablished classical approaches for predictive maintenance but a systematic inclusion of smartness to this context is still missing in the field. In this study, we propose a Smart Predictive Maintenance framework enabled by key Industrial Artificial Intelligence technologies of Digital Twins and Smart Big Data. The framework includes steps of Digital Twin development along with the utilization of Smart Big Data in the sense of Predictive Maintenance along with the application of the frontier to an important problem of RF Impedance Matching.
Implementation of EIGRP Using Packet Tracer

Routing Protocols are extremely important in today's internet world. Routing is the method of selecting a route for the traffic within the network, as well as between and across several networks. Routing protocols are required by routers in order for the network to function properly. Routers become aware of all potential routes and combine them to their routing table in such a way that the routing process can forward (route)the routable data [? ]. IP is one of these protocols. Routers are forwarded using an efficient routing mechanism through an update packets from a initial node to a final node optimum way and does not broadcast several routing updates at the same time. As a result, the network's convergence is maintained. In this paper, we'll look at how to route using an EIGRP-based routing system protocol A network simulation software called “packet tracer” used a Cisco router network architecture[2][14]. The ping command is used to verify the results. The procedure of assigning IP addresses and their setting in routers and computers is also covered in the paper.
A Novel Nodule Tracking System and Convolutional Neural Networks for Medical Internet of Things

Medical and health care is a significant application field for Internet of things technologies. The medical Internet of Things (IoT) is employed in this research to collect data from clinical trials of cancerous and non-cancerous lung nodules, and intelligent diagnostic strategies are studied as a result. Detecting cancer in its early stages offers the patient a highly improved survival rate and various treatment options. Oncologists use CT scans as one of the primary screening techniques to aid in cancer cell identification. This research aims to establish a new methodology to extract and detect nodules residing in lung regions using image processing and tracking techniques. Our proposed system starts with segmenting lung regions by detecting and processing each side individually for efficient preservation of suspicious nodules located on the edge of the lung parenchyma. This is followed by tracking and listing every candidate that occurs in the screening image of the patient to extract highly suspicious nodules that pass finally through two CNN models to distinguish True Nodules. The proposed method gives a high accuracy rate compared to other methods (around 95%). That is why it is one of the recommendations for efficient outcomes. Our database in this approach is the Lung Image Database Consortium image collection (LIDC-IDRI).
A GSM Band RF Sensor Antenna

An RF sensor antenna for GSM 800 and GSM 1800 band is presented in this work for measuring the ambient radiations from nearby base stations or mobile handsets. A dual resonance is obtained by cutting an E-type slot on a spatula-shaped patch. Measured peak gain varies from 0.6-1.65 dBi in lower band and 2-3.2 dBi in upper band. The antenna factor over the frequency band varies from 26 to 33.2 dB/m.
A Study on CNN Model with Multiple Inputs for Detection of Driving Area

In this paper, we propose a method to distinguish the drivable and non-drivable areas of a mobile robot by learning road driving image data of an autonomous vehicle. Through recognition of the drivable area of vehicles and mobile robots, it is intended to improve performance more stably when driving and mapping. We propose a CNN model for segmenting the driving area by learning road driving image data. The proposed model proceeds with a performance evaluation of the model through one input and multiple inputs. We shows that the proposed model preserves spatial information and reduces the network parameters.
A Modified Approach to Optimize Phase-Gradient Metasurface-Based Beam-Steering Systems

In this work, we compare two different simulation environments to aid in optimizing Phase-Gradient Metasurfaces for applications in beam-steering antenna systems. Such a strategical arrangement of supercell for optimization reduces the simulation cost and time and enhances the design efficiency. A Floquet-analysis-based optimization approach is followed to reduce the grating lobes in a Near-Field Meta-Steering system.
Real-Time Polling Task: Design and Analysis

Usually, robotic systems must be designed as reactive systems because they must be able to react and adapt to their environment and also communicate with some other systems. However, the reactive behavior, because of its non-deterministic nature, is difficult to analyze and prevents designers to perform a proper real-time analysis which is usually needed for critical robotic systems. In this paper, we propose a deterministic task model to handle the reactive behavior as well as the necessary tools to analyze it and verify the respect of real-time constraints. An implementation of this model, which is used in a ROS2 patch, is also presented.
A Passing Predicting Model for Autonomous Vehicles at a Mixed-Driving Signaled Intersection

Emerging technologies like autonomous driving and vehicle-to-everything can bring improvement in passing predicting at a mixed-driving signaled intersection. However, there are neither models to implement them into prediction tasks, nor datasets to use for researching it. In this work, we design three models centered around platoons for data sharing among autonomous vehicles and generate related datasets by simulation. Then, we design three passing predicting models based on machine learning methods and train them with our datasets. Results show that our best passing predicting model based on gate recurrent unit reduces the predicting passing time of mean absolute error with 84.42% compared to the existing baseline models. The average passing predicting precision is now below a half-second. And experiments also demonstrate that our model has excellent generalization on different unseen traffic conditions. Finally, we release our dataset and models as the OpenMixIntersection.
The Mechanism and Evaluation of Various Wireless Charging Technologies

As the modern world requires electrical charging to function in many ways, charging technology is developing in a fast pace. This review paper hence mainly focuses on the summary and evaluation of various wireless charging technologies. A brief introduction of the background of wireless charging, concerning the gradual development and main features of this new charging technique, is followed by detailed explanation of the fundamental mechanism of three most common wireless charging methods: inductive power transfer, capacitive power transfer, and magnetic resonance. Afterwards, the pros and cons of each specific method is presented, and some solutions and applications of such methods are given, aiming to optimize the utility of wireless charging technologies. In the end, a conclusive table is also constructed which effectively summarizes the current analysis of the specific characteristics of wireless charging technologies, which may guide better societal application of technological improvements in more efficient charging experience in the real world.
Local bivariate Cauchy distribution for video denoising in 3D complex wavelet domain

In this paper, we present a new video denoising algorithm using bivariate Cauchy probability density function (pdf) with local scaling factor for distribution of wavelet coefficients in each subband. The bivariate pdf takes into account the statistical dependency among wavelet coefficients and the local scaling factor model the empirically observed correlation between the coefficient amplitudes. Using maximum a posteriori (MAP) estimator and minimum mean squared estimator (MMSE), we describe two methods for video denoising which rely on the bivariate Cauchy random variables with high local correlation. Because separate 3-D transforms, such as ordinary 3-D wavelet transforms (DWT), have artifacts that degrade their performance for denoising, we implement our algorithms in 3-D complex wavelet transform (DCWT) domain. In addition, we use our denoising algorithm in 2-D DCWT domain, where the 2-D transform is applied to each frame individually. The simulation results show that our denoising algorithms achieve better performance than several published methods both visually and in terms of peak signal-to-noise ratio (PSNR).
A Wideband High Gain Arched Slotted Patch Array Antenna by Substrate-Integrated Coaxial Line

In this paper, a millimeter-wave wideband high gain arched slotted patch array antenna by substrate-integrated coaxial line (SICL) has been designed. Initially, antenna array is composed of a four-way power divider network and four arched patch elements, and its size is 8.41$\lambda_{0} \times 4.13\lambda_{0} \times 0.09\lambda_{0}$. Four arched E-shaped slotted patches with different orientations are adopted to improve the impedance bandwidth. A feeding structure is consisted of two two-way T-junctions and a 180° delay line to feed these patches, while the element excites 45° purified linearly polarized wave. Then, three two-way T-junctions with quarter-wavelength impedance transformer as the four-way power divider provide the equal amplitude and uniform phase to each element for maximizing the gain of the array. Over the frequency range of 39.7-44.4 GHz, the return losses are less than 10 dB, and the maximum peak gain is 16.0 dBi. Finally, these simulated results show that the proposed array antenna have the characteristics of high gain and wideband.