Miracle-dz/UniFAR-FTUs huggingface dataset:
Identi ﬁ cation of the drivers of and barriers to COVID-19 vaccine intake behavior using a mixed-method design: implications from a developing country

Various COVID-19 vaccines are available across the world. However, short phases of clinical trials for emergency use and myriad rumors about and misinformation on vaccines spread through different media sources, induce confusion and trigger vaccine hesitancy behavior. Although clinical trials demonstrated promising results in the ability of vaccines to protect, social endeavor is required for vaccines to succeed. Mitigation of the pandemic is only possible through widespread acceptance of the vaccine. Thus, identifying the factors that impact vaccine intake is crucial. This study determined factors that affect intentions toward vaccination using a mixed-method approach, in which qualitative and quantitative analyses were conducted together. Analysis of variance (ANOVA) is used in the quantitative study, and content analysis is employed in the qualitative study. The ﬁ nal sample of this mixed-method study consists of 568 participants for the quantitative study and 237 for the qualitative study. They were selected using an online questionnaire. Findings suggest that social, psychological, attitudinal, perceptual, and informational factors play a crucial role in shaping people ’ s intentions toward vaccines, ultimately in ﬂ uencing their decision to accept or reject vaccination. Results of both the qualitative and quantitative studies, in parallel with each other, indicated similar barriers against and drivers toward vaccine intake behavior, providing strong insights into the reasons behind vaccine acceptance and hesitancy. Based on the insights gained in this study, a set of recommendations were compiled for policymakers. © 2023 The Author(s). Published by Elsevier España, S.L. U. on behalf of Journal of Innovation & Knowledge. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
Low Temperature Loss-Analysis of SiC MOSFETs for Integrated Motor Drive Applications

The development of electrical machine drives requires the assessment of semiconductor device performance over the entire anticipated operating temperature range, which includes cold start and arctic conditions alongside elevated temperature conditions. In this paper, the development and commissioning of an experimental testbench for characterising MOSFETs at cold temperatures down to -45 °C is presented. The experimental hardware allows for rapid acquistion of data that is not readily available from manufacturer datasheets at present. Results are presented for nine different 3rd generation SiC MOSFETs with voltage ratings between 650 V and 1.7 kV.
Long-Time Temperature Forecasting for Power Plant Boiler Based on Data-Driven Model

The temperature of the incinerator plays a critical role in ensuring the efficiency and safety of thermal power generation units. Accurate temperature prediction models are essential for controlling furnace combustion efficiency and detecting abnormal combustion states. This study aims to develop an advanced data-driven model that addresses the challenges associated with long-term temperature forecasting. The proposed model adopts an encoder-decoder architecture that integrates a multi-scale temporal vector and a partial attention vector, enabling the model to learn correlations effectively. Combining a Temporal Convolutional Network with a Gate Recurrent Unit as the encoder, the model can adaptively capture the underlying relevance and long-term dependencies among multiple variables in a furnace combustion system. To evaluate the performance of the proposed models, a real-world dataset from a waste-to-energy plant was utilized. The results demonstrate remarkable performance, with a root mean squared error of 3.74 and a mean absolute error of 2.38 in a 30-step prediction. These findings underscore the superiority of our model as an optimal solution for temperature prediction modeling.
Simplified Wide-Band Frequency Models for Single and Multi-Layered Boost Inductors

Three phase boost power factor correction circuits (PFCs) are a commonplace in grid-tied applications. Typically, a compensating line inductor is added in series with the rectifier to perform the necessary boost functionality, reactive power control and filter line harmonics. Therefore, the impedance characteristics of the line inductor i.e., the boost inductor, is critical from (Electromagnetic Interference) EMI compliance point of view in such a generic PFC system. This paper specifically addresses the anti-resonance phenomenon observed with traditional multi-layered winding of a boost inductor. A 3-port model for such multi-layered boost inductors is developed to capture this phenomenon accurately. Based on the initial findings, single-layered edge-wound inductor solution is proposed to eliminate the anti-resonance mode. Consequently, modeling procedure for edge-wound inductor is developed. Simulation and experiment results are performed using a three-phase three-level rectifier test bed to verify the effectiveness of the proposed solution over a traditionally wound boost inductor. In summary, a quantitative comparison of different solutions is shown which shows the edge-wound coil to be the superior candidate.
Topology Optimization of an Alternate Permanent Magnet Electrodynamic Suspension

This paper presents the topology optimization of an alternate permanent magnet electrodynamic suspension in a plane parallel to the track, using therefore a semi-analytical 3-D model. This model is based on the superposition of single permanent magnets to compute the magnetic field of more complex structures. The optimization, divided in three steps, showed that new permanent magnet arrangements can significantly increase the lift-to-drag ratio of the suspension at the expense of a low reduction of the lift-to-weight ratio.
Physician Behaviors and Blockchain Possibilities Implementation on the Intention to Use Telemedicine Applications

Telemedicine apps are commonly utilized by physicians and patients in the current digital era. The main problem is that using various telemedicine features in daily practice presents many challenges for doctors. Additionally, there are issues with patient data needing to be integrated and issues with validating digital prescriptions, which may impact the degree of intention to use. The research aims to understand and investigate how physician behavior and blockchain adoption affect telemedicine application usage intentions. This study employs a quantitative review methodology, multivariate statistical analysis, and partial least squares structural equation modeling (PLS-SEM). Two hundred four respondents are physicians from Indonesia who responded to the questionnaires distributed using Google Forms with the Purposive Sampling Method. The data was collected in April 2023. The data processing results revealed that all variables had t-values greater than 1.96 and ranging from 2.130 to 8.056, showing that all the variables, including the major variables–perceived usefulness, perceived ease of use, blockchain adoption, and contamination avoidance–were significant. The results of this study indicate that doctors want to keep utilizing telemedicine applications since they can make it simpler to deliver healthcare services, are easy to use, and can be further enhanced by implementing blockchain technology. It showed in all six significant hypotheses testing.
Pitfalls of Using Passivity Index to Guide Grid-connected Inverter Control Design in Low-frequency Region

The main purpose of this article is to elaborate on the pitfalls of using the frequency-domain passivity theories in grid-inverter interactions within the low-frequency range. It mainly covers the relationship between the passivity index and stability, considerations for control design guided by optimizing the passivity index, and issues with a negative infinite index. It is advised to exercise caution when applying passivity theory in the low-frequency range. These conclusions have been substantiated through numerical and experimental studies.
Optimizing Wrist-Based Bioimpedance: The Role of Electrode Type, Positioning and Signal Frequency in Health Monitoring

Bioimpedance spectroscopy is a non-invasive technique that offers insights into physical parameters such as heart rate, fat-free mass, extra/intra-cellular, and water retention. When integrated with wrist wearables, it holds potential for personalized health monitoring. Electrode type, positioning, and tightness of the wearable heavily influence signal characteristics. This complexity makes it challenging to draw conclusions across various setups, highlighting the need to quantitatively assess recording parameters. Such assessments not only aid in effective modeling but also promote diversification of experimental setups. In this work, the impact of electrode positioning and type on wrist-based bioimpedance measurements was investigated. The effects of input signal frequency and pressure applied on electrodes were also considered, using a custom pediatric blood pressure cuff to simulate wearable tightness. Additionally, acquisition-dependent drift on bioimpedance measurements was explored. A one-way ANOVA analysis for dry electrode positioning revealed significant impedance differences (p<0.05)* at both 100Hz and 1000Hz. In contrast, wet electrode positioning showed highly significant impedance differences (p<0.01)** at 1000Hz.
Threshold Analysis for the E-Polarized Modes of Microsize Graphene Strip Grating Laser

We consider the threshold conditions for the E-polarized lasing modes of infinite flat grating of graphene strips lying on dielectric-slab substrate. The conductivity of graphene is modeled using the Kubo formulas, while the substrate material is assumed to have gain that offsets the radiation and ohmic losses and allows the modes to reach the lasing threshold. We look for the frequency and the gain index threshold value, specific to each mode, as eigenvalues. To find them, we reduce the field problem to a dual series equation for the complex amplitudes of the Floquet spatial harmonics, which we analytically regularize based on the use of Discrete Inverse Fourier Transform. Then, the problem reduces to a Fredholm 2nd-kind matrix equation for the unknown Floquet harmonic amplitudes. Therefore, the convergence of the resulting code is guaranteed by the Fredholm theorems. Since plasmon modes are absent in the case of E-polarization, we study the high-threshold modes of the substrate slightly perturbed by the presence of grating and ultra-low-threshold lattice modes of the entire grating as a periodic open cavity. Our results show that the lattice modes are mediated by the substrate guided waves.
Estimating and enlarging the domain of attraction for a nonlinear system with input saturation

This paper focuses on the stabilization problem of a nonlinear system subject to actuator saturation. Such that the results are based on the differential algebraic representation and use of a convex hull description subject to the saturation effects. The contribution of this work is to estimate enlarging domain of attraction. Therefore, for find the largess domain of attraction, the block matrix-variable will be chosen. Numerical examples are provided to illustrate the efficiency of this new approach.
Back-EMF Based Self-Sensing Vector Control for Ultra-High-Speed Surface Mount PMSM

This paper presents an improved rotor position estimation algorithm for ultra-high-speed surface mount permanent magnet synchronous machines (SPMSM). The proposed approach combines a synchronous frame back-EMF state filter and a motion observer structure to accurately estimate rotor position at speeds above 80 kRPM, where conventional position sensing techniques struggle. The state filter and motion observer are designed directly in the discrete domain considering necessary latch dynamics and computational delays. Simulation results for a 2-pole SPMSM show stable performance and close to zero phase lag position estimation at a fundamental frequency of 2.6 kHz. The implementation ensures stable control performance at sampling to fundamental frequency ratios as low as 5. Experimental validation of the proposed algorithm has been presented. The proposed methodology is a significant advancement toward precise and reliable rotary motion state estimation for ultra-high speed SPMSMs.
Soft-bottom benthic assemblages and levels of contaminants in sediments and biota at Gray's Reef National Marine Sanctuary and nearby shelf waters off the coast of Georgia (2000 and 2001)

A series of studies was initiated to assess the condition of benthic macroinfauna and chemical contaminant levels in sediments and biota of the Gray’s Reef National Marine Sanctuary (GRNMS) and nearby shelf waters off the coast of Georgia. Four key objectives of the research are (1) to document existing environmental conditions within the sanctuary in order to provide a quantitative benchmark for tracking any future changes due to either natural or human disturbances; (2) to examine broader cross-shelf spatial patterns in benthic fauna and sediment contaminant concentrations and to identify potential controlling factors associated with the observed patterns; (3) to assess any between-year temporal variability in benthic fauna; and (4) to evaluate the importance of benthic fauna as prey for higher trophic levels. Such questions are being addressed to help fulfill long-term science and management goals of the GRNMS. However, it is anticipated that the information will be of additional value in broadening our understanding of the surrounding South Atlantic Bight (SAB) ecosystem and in bringing the knowledge to bear on related resourcemanagement issues of the region. We have begun to address the first three of these objectives with data from samples collected in spring 2000 at stations within GRNMS, and in spring 2001 at stations within the sanctuary and along three cross-shelf transects extending from the mouths of Sapelo, Doboy, and Altamaha Sounds out to sanctuary depths (about 17-20 m). This report provides a description of baseline conditions within the sanctuary, based on results of the spring 2000 survey (Section II), and uses data from both 2000 and 2001 to examine overall spatial and temporal patterns in biological and chemical variables within the sanctuary and surrounding inner-shelf environment (Section III). (PDF contains 65 pages)
Vector-sensing antenna for measuring the direction of arrival of ionospherically propagated HF radio signals

The ability to determine the direction of incident radio waves is useful in a wide variety of different applications. For high frequency (HF) radio waves, this is commonly done using wide aperture arrays of antennas. However, due to the long wavelengths of HF waves, such arrays must be physically large. A vector-sensing antenna system, using orthogonal triads of antennas to measure the electric and magnetic field components of the incoming waves, offers the possibility of making these measurements without requiring a large area or a large number of elements. In this paper we will look at the performance of various vector-sensing antenna configurations when used to receive HF radio signals, and explore some of the difficulties that may be encountered.
An Overview of the Energy Efficiency and Power Management Strategies in Robots

The recent advances in Mobile Robots (MRs) have engendered the need for energy efficient performance. To achieve the latter, two worthwhile aspects come into play: gait planning and Energy Storage Systems (ESSs). These branch into gait parameter optimization, where the robot’s locomotion is rendered more efficient, ESS management, where the power providing unit is studied, and charge scheduling, responsible for handling MRs’ task allocation. Optimal and energy efficient gait synthesis was achieved through multi-objective optimization algorithms, while the robot’s ESS performance and charging schedules were studied by applying effective modeling techniques and algorithms. This paper offers an overview of recent works in the mentioned topics and aims to facilitate future possible research.
A Review of Target Tracking Based on Deep Learning

Target tracking has always been a research hotspot in the field of computer vision. This article focuses on single-object tracking and introduces the application and development of deep learning in target tracking. Single-object tracking, as a technique that analyzes the motion state and provides localization for individual objects, has been widely applied in areas such as human-computer interaction, autonomous driving, and intelligent surveillance. However, practical applications have been slow to develop due to challenges such as lighting variations, object occlusion, and background interference during the tracking process. In recent years, with the significant success of deep learning in image classification, semantic segmentation, and object detection, an increasing number of researchers have applied deep learning frameworks to target tracking tasks. This paper provides a comprehensive review of popular deep learning-based target tracking algorithms from six perspectives: traditional tracking methods, correlation filters, convolutional neural networks, siamese neural networks, generative adversarial networks, and attention mechanism networks. Additionally, it summarizes the most representative datasets in the field of object detection, analyses the representative experimental results and explores the future trends in the field of single-object tracking.
Study of the Optical Properties of Complex Ice Crystal Aggregates.Application to the remote sensing of dry and mixed-phase snowfall

Snowfall is a prominent component of the Earth’s hydrologic cycle. Global observations of snowfall are essential for the monitoring of the status of the Earth system, and because of their wide coverage, nowadays, remote sensing instruments are fundamental tools in the measurement of precipitation. The principal uncertainty in the interpretation of radar data are the scattering properties of the hydrometeors which are strictly connected to their microphysical characteristics. The presented study propose a comprehensive approach that analyze the all snow physical characteristics: single particle modeling, snowfall automatic microphysical retrieval, scattering simulations and remote sensing. A state of the art snow aggregation algorithm (SAM) has been implemented to model the snowflake accurate morphology, simulating the basic physical governing phenomena of snow formation and growth. The algorithm has been further extended to model the initial stage of snowflake melting. The snowflake models are used as input of computer scattering simulations. The analysis of the radiative properties obtained with the spherical models and the complex aggregated particles produced by SAM shows that the former are inadequate to represent the scattering characteristics of large aggregated particles. An innovative methodology has been developed to automatically estimate the mean snow mass-size relation using particle size distribution, velocity fits, snow accumulation and Rayleigh radar reflectivity. The radar reflectivities at Ka and W band simulated with T-matrix spheroidal models and using the retrieved mass-dimensional relation cannot match the observation. When the same simulation is performed with the usage of DDA scattering calculations the results reproduce better the observed radar reflectivities. This outcome gives validity to both the microphysical and the scattering model. A multi-perspective approach, that simultaneously include the microphysical and scattering simulation of snowflake properties, is the way forward to solve the uncertainties related to snowfall remote sensing.
Study of Degradation Mechanisms in Hydrogen-Terminated Diamond MOSFETs Under Off-State and Semi-on-State Conditions

The study of degradation mechanisms is important to improve the reliability of hydrogen-terminated (C-H) diamond metal-oxide-semiconductor field-effect transistors (MOSFETs). In this article, the trapping effects of C-H diamond MOSFET under off-state and semi-on-state conditions are studied by pulsed characterization. The results show that the on-resistance $(R_{\text{ON}})$ increase with the increased intensity of the drain quiescent bias (VDSQ) under off-state conditions; the further increase in RON under semi-on-state conditions due to the hot-carrier effects. Furthermore, under off-state conditions, the increase in threshold voltage (VTH) is mainly due to the reverse gate quiescent bias (VGSQ) and the increased intensity in $V$DSQ, which promotes the detrapping effects under the gate region. In addition, the decrease in $V_{\text{TH}}$ under semi-on-state conditions is caused by the weakness of detrapping effects and the enhancement of hot-carrier effects.
Resting-State EEG Classification of Children and Adolescents Diagnosed With Major Depressive Disorder Using Convolutional Neural Networks

The aim of this research was to use Convolutional Neural Network (CNN) based on VGG16 model to identify biomarkers in Children and Adolescents with Major Depressive Disorder (MDD) from age-matched healthy young individuals. For this purpose, resting-state eyes-closed electroencephalography (EEG) was pre-processed, and analysis was performed based on frequency bands and Regions Of Interest (ROI). Overall, results achieved an accuracy of 0.875 and F1-Score of 0.638, and further revealed lower Delta activity (0.856 accuracy and 0.539 F1-Score), higher Theta activity (0.895 accuracy and 0.717 F1-Score), and higher Alpha activity (0.804 accuracy and 0.606 F1-Score) which were classified in MDD compared to healthy individuals. In this study, Beta and Gamma activities were not found as biomarkers for MMD with higher accuracy. Moreover, results showed that while in MDD group, Delta frequency bands were featured in left temporal, occipital, bilateral frontal, and central regions; Theta frequency bands were featured in left temporal and frontal, left occipital, and central regions. The Alpha frequency band was further featured in left frontal, central, left occipital, and left temporal regions.
Higher-Order Topology and Fully Flat Bands in Multimode Photonic Time-Reversal-Invariant Lattices

Higher-order topological insulators (HOTIs) are unusual phases of matter hosting robust boundary-localized states of at least two dimensions less than the effective dimensionality of the sample. Their photonic realizations span a remarkably large spectrum of distinct HOTI phases, owing to a high versatility of such platforms. Furthermore, even more exotic phases could be engineered if the system additionally hosts flat bands in the spectrum. The latter may lead to stronger simultaneous localization of corner and bulk states, paving the way towards enhanced control of light propagation in photonic lattices. However, to date most designs involve either geometrically distorted photonic lattices or external effective magnetic flux constructed artificially by using additional off-resonant elements. In this work, we propose a different approach utilizing the interference of accidentally degenerate modes of the meta-atoms, which may lead both to unconventional higher-order topology and to nearly flat bands for geometrically undistorted time-reversal-invariant photonic lattices. We illustrate our proposal by characterizing three such models and outlining their similarities and unique features. Our approach could be harnessed to construct simple photonic designs with topologically and symmetry- protected control over the light propagation.