Miracle-dz/UniFAR-FTUs huggingface dataset:
Numerical investigations of the electromigration boundary value problem

The electromigration diffusion boundary value problem with the perfectly blocking diffusion barrier is numerically investigated. Three possible boundary conditions are identified as physically meaningful and the solutions compared at the blocking barrier. It is seen that the solution of M. Shatzkes and J. R. Lloyd [J. Appl. Phys. 59, 3890 (1986)] is a good approximation for the time to failure if the critical vacancy concentration for failure is not too near a steady‐state value. A dimensionless parameter is introduced which may be useful in estimating ultimate electromigration performance.
Adaptive Neuro-Fuzzy Inference System in Fuzzy Measurement to Track Association

The main issue in a surveillance environment is the target tracking. The most important concern in this problem is the association of the various measurements with the existing target tracks. The fuzzy c-means data association (FCMDA) algorithm, based on the fuzzy c-means (FCM) algorithm, is an efficient solution for the problem of measurement to track association in a multisensor multitarget environment. It has a high accuracy in measurement to track association when targets are far from each other. However, its accuracy remains low when targets are close to one another. The FCMDA algorithm performance is usually lost in this environment, especially when measurement noise is high. In the FCMDA algorithm, the association between measurements and tracks is determined using an optimal membership function derived from the FCM algorithm for the fixed predicted state of targets. The prediction of the target state deviates from its correct value based on updating the tracker/filter with the wrong associated measurement. Consequently, the wrong association can take place using a deviated prediction of target state in the FCMDA algorithm. In this paper, to overcome this shortcoming of the FCMDA algorithm, the predicted state of every target in a surveillance environment is compensated for the effect of wrong associated measurement by an adaptive neurofuzzy inference system (ANFIS). An ANFIS has both the advantages of expert knowledge of a fuzzy inference system and the learning capability of neural networks. So a trained ANFIS is able to compensate the effect of a wrong associated measurement on the prediction of target state. Using the compensated prediction of target state in the FCMDA algorithm can always save the performance of the FCMDA algorithm and extend its domain of usage in practical applications. The simulation results demonstrate that considerable improvements in terms of accuracy and performance are achieved by using the compensated prediction of target state in the FCMDA algorithm.
A physiological articulatory model for simulating speech production process

A physiological articulatory model has been developed to simulate the dynamic actions of speech organs during speech production. This model represents the midsagittal region of the tongue, jaw, hyoid bone, and the vocal tract wall in three dimensions. The soft tissue of the tongue is outlined in the midsagittal and parasagittal planes of MR images obtained from a male Japanese speaker, and constructed as a 2-cm thick layer. The palatal and pharyngeal walls are constructed as a hard shell of a 3-cm left-to-right width. The jaw and hyoid bone are modelled to yield rotation and translation motions. The muscle structure in the model is identified based on volumetric MR images of the same speaker. A fast simulation method is developed by modeling both the soft tissue and rigid organs using mass-points with two types of links: viscoelastic springs with a proper stiffness for connective tissue, and extremely high stiffness for bony organs. Muscle activation signals are generated by a model control strategy based on the target-reaching task, and then fed to drive the model to approach the targets. The model demonstrated realistic behaviors similar to coarticulation in human speech production (Dang and Honda, 1998, 1999, 2000).
AR IN VR: OMNISTEREOSCOPIC TELEPRESENCE WITH HOLOGRAMS FOR REMOTE MAINTENANCE AND COLLABORATION

With the development of mixed reality technologies, remote maintenance and collaboration applications receive increasing interest in the industry. The combination of 360-degree telepresence and augmented reality (AR) cues have been shown to be an effective way of remote collaboration. However, most existing methods do not provide depth perception, which can improve the remote inspection. Also, many methods that use holograms for live collaboration are limited with only pointing functionality. In this paper, we present a 360-degree telepresence system interacting with an AR backend to create an immersive mixed reality interface for remote collaboration. The proposed camera system does not need to be carried by any on-site person, but can be controlled by the remote operator intuitively. We deploy network delay compensation methods and propose novel projection strategies for a correct and efficient rendering. A comparative analysis shows advantages of the proposed system over previous work, and that it is a promising approach for improving remote collaboration and maintenance. Our experiments indicate that our system has a glass-to-glass delay of roughly 106 ms, and a data rate to the client varying between 10-15 Mbps, which can be further optimized for specific applications. Exploratory tests with VR-experienced users showed that the motion-to-photon latency of the system is in acceptable ranges. The system can be deployed in various industrial applications for a live or asynchronous collaboration between an on-site user (with AR) and an off-site user (with AR in VR).
Investigations of Nonlinear Vibrations for Cantilever Beam

The nonlinear vibrations of two flexible cantilever beams with different sections, which are subjected to harmonic vertical base excitation, are studied experimentally. The periodic motions and chaotic motions are demonstrated. And the multi‐pulse orbits, which imply the existence of chaos under the Smale horseshoes sense, are also observed.
CALCIUM ISOTOPE COMPOSITION OF METEORITES, EARTH, AND MARS

The relative abundances of calcium isotopes in the mass range 40–44 were measured in primitive and differentiated meteorites and igneous rocks from Earth and Mars in search of non-mass-dependent variations that could provide clues about early solar system processes. Most bulk samples of planetary materials have calcium isotopic compositions identical with Earth's within the current resolution of about 0.01% in 40Ca/44Ca. Possible exceptions include carbonaceous chondrites, some ordinary chondrites, and two samples of calcium–aluminum-rich inclusions, which have small excesses of 40Ca. The samples with 40Ca excesses are also known to have 50Ti and 135Ba excesses and 142Nd and 144Sm deficits. Collectively these data from refractory elements suggest that the planetary embryos represented by chondrites preserve isotopic heterogeneity that reflects different nucleosynthetic sources. No late admixture from a single nucleosynthetic source can explain all observations. The results are most compatible with variable proportions of material derived from Type II supernovae. The initial calcium isotope compositions of Earth and Mars are indistinguishable and similar to the 40Ca abundance found in some chondrites and all differentiated meteorites studied. It appears that isotopic heterogeneity in calcium was still present at the completion of disk formation but was homogenized during planetary accretion.
Powerful nonparametric statistics to compare k independent ROC curves

The authors deal with the problem of comparing receiver operating characteristic (ROC) curves from independent samples. From a nonparametric approach, they propose and study three different statistics. Their asymptotic distributions are obtained and a resample plan is considered. In order to study the statistical power of the introduced statistics, a simulation study is carried out. The (observed) results suggest that, for the considered models, the new statistics are more powerful than the usually employed ones (the Venkatraman test and the usual area under the ROC curve criterion) in non-uniform dominance situations and quite good otherwise.
The Variation of Phase Along the Basilar Membrane with Sinusoidal Vibrations

An optical means of measuring phase relations in the cochlea is described. With the stapes set into sinusoidal vibration, the phase relations, measured along the length of the cochlear partition, show that a traveling wave has been set up. The form of resonance which appears does not correspond at all with that to be found in a simple vibrating system. Measurements are presented for a frequency of 200 c.p.s. of the amplitude and phase of vibration at various places along the cochlear partition. From these is derived a curve showing the longitudinal bending of the partition with the traveling wave at two different points in time.Very small changes in the form of vibration can easily be demonstrated by the measurements of phase. By this means it is shown that the basilar membrane cannot be released from tension since it is not normally under tension. The elastic properties of the various distinguishable tissues within the cochlear partition were measured. These measurements show that only the elasticity of .
The Use of Electrochemical Impedance Spectroscopy as a Tool for the In-Situ Monitoring and Characterization of Carbon Nanotube Aqueous Dispersions

So far, there is no validated technology for characterizing the dispersion and morphology state of carbon nanotubes (CNTs) aqueous dispersions during sonication. Taking advantage of the conductive nature of CNTs, the main hypothesis of the current study is that Electrochemical Impedance Spectroscopy (EIS) is an appropriate technique for the in-situ monitoring and qualification of the dispersion state of CNTs in aqueous media. To confirm our hypothesis, we monitored the Impedance |Z| during the sonication process as a function of type CNTs/admixtures used for the preparation of the aqueous solutions and of crucial process parameters, such as the applied sonication power and duration (i.e., sonication energy). For dispersions above the percolation threshold, a drop of |Z| by approximately seven orders of magnitude was observed, followed by a linear reduction. The dramatic change in |Z| is regarded as an indication of the formation of a conductive path or destruction of an existing one during sonication and can be used to characterize the dispersion and morphology state of CNTs. The results of the EIS provide, straightforwardly and reliably, the required information to create an optimum dispersion protocol for conductive CNT suspensions. The produced dispersions are part of research focusing on the manufacturing of cement-based composite materials with advanced thermoelectric functionalities for energy harvesting. Such dispersions are not only limited to energy harvesting applications but also to applications where functionalities are introduced through the use of conductive-based suspensions.
The Effect of Acacia nilotica on Helicobacter pylori Colonization

: The biological activity and the effects of aqueous Acacia nilotica leaf extracts on H. pylori colonization factors were investigated. The extracts were assessed for antimicrobial, anti-urease and anti-adhesion properties against H. pylori using a combination of in vitro, in situ, and in vivo assays. Acacia nilotica showed moderate antimicrobial activity against H. pylori with a minimum inhibitory concentration and minimum bactericidal concentration of 180 µg/mL and 5 mg/mL respectively. The total phenolic and flavonoid contents in Acacia nilotica extract were 154.0 mg gallic acid equivalent (GAE)/g and 8 mg quercetin equivalent (QE)/g of dry extract. The extract also showed a high antioxidant activity of 312 mg ascorbic acid equivalent/g extract. GC-MS extract analysis showed 2-Cyclohexen-1-one-4,4-d2, 6,6-dimethyl-(28.10%), 2,5,5-trimethyl-1-cyclopent-2-enon (14.00%) and -2-methyl(3-D) propenoic acid (9.42%) as the major constituents. The extract also showed high anti-urease activity and high anti-adhesion property both in vitro and in vivo. The results of this study showed that Acacia nilotica can affect the colonization of H. pylori in the stomach, and can be further explored for its pharmaceutical application for the H. pylori eradication.
Beyond Bothered: Exploring Identity, Stressors, and Challenges of Black Women Ivy Collegians

Research exploring the experiences of Black women attending predominantly White institutions in the United States is emerging. However, scholarship centering Black women’s experiences within Ivy League institutions remains elusive. This study explores the experiences of six Black women Ivy League collegians and their perceptions about how race, gender, and ethnicity shaped their sense of identity and campus interactions while attending an Ivy League institution. Using Black feminist thought as the theoretical framework, findings illustrate salient stressors and challenges while navigating interactions with peers, professors, and other university personnel. This study’s findings broaden our understanding of Black women college student experiences within Ivy League institutions and informs institutions of higher education on strategies to best support them.
Certified defences can hurt generalisation

In recent years, much work has been devoted to designing certified defences for neural networks, i.e., methods for learning neural networks that are provably robust to certain adversarial perturbations. Due to the non-convexity of the problem, dominant approaches in this area rely on convex approximations, which are inherently loose. In this paper, we question the effectiveness of such approaches for realistic computer vision tasks. First, we provide extensive empirical evidence to show that certified defences suffer not only worse accuracy but also worse robustness and fairness than empirical defences. We hypothesise that the reason for why certified defences suffer in generalisation is (i) the large number of relaxed non-convex constraints and (ii) the strong alignment between the adversarial perturbations and the “signal” direction. We provide a combination of theoretical and experimental evidence to support these hypotheses.
Reduced Order PEEC Modeling for EMC Problems via Mixed Arnoldi Algorithm and Padé Approximation

The Partial Element Equivalent Circuit (PEEC) method is a 3-D full-wave modeling method suitable for combined electromagnetic and circuit analysis, and now is one of the promising numerical methods for Electromagnetic Compatibility (EMC) modeling. Model order reduction (MOR) techniques provide a feasibility to approximate complex circuit models with compact reduced-order models, and have great potentials to improve the computational efficiency for complex modeling problems. An effective MOR technique basing on mixed Arnoldi algorithm and Padé approximation for the PEEC modeling is introduced in this paper. Numerical simulations of a typical coupled micro-strip line EMC problem indicate the effectiveness of the proposed methods.
Research on compression method of yolov5 model based on channel pruning

With the development of high-resolution and wide -format SAR satellites, on-board real-time processing of massive data has become a major development trend. However, due to the limitation of on-board resources, on-board real-time processing is faced with insufficient storage space, insufficient operating memory, and a large amount of computation. In order to solve this problem, this paper conducts channel pruning for the large target detection model yolov5 to achieve model compression, uses the bn layer as the basis for pruning, and adaptively determines the pruning ratio of each layer on the basis of analyzing the pruning sensitivity. We experimentally determine the best retraining strategy. In the end, this paper obtained the yolov5 lightweight network model with 5 configurations, the parameter reduction range is 74%-93%, the computation volume reduction range is 49.4%-89.6%, and the Map floats -3.6%-0.6% compared with before pruning. It is proved that the pruning scheme proposed in this paper has both large compression ratio, high precision and flexibility, and has important reference significance for the real-time target detection task on board.
Digital Realization of Conductance-Based Adaptive Exponential Integrate-and-Fire Neuron Model

The field of neuromorphic engineering is a multidisciplinary research topic which incorporates the knowledge of Neuroscience, physics, biology, and electronic engineering in order to replicate the neurological patterns of human brain. This paper presents a digital design based on CORDIC algorithm for a biologically inspired neuron model called Conductance-Based Adaptive Exponential Integrate-and-Fire (CAdEx) neuron model which is recently proposed by Gorski. The low-cost hardware implementation for the proposed model is capable of reproducing the same spiking activity of the original biological model. The modified digital architecture has been physically implemented on field programmable gate arrays. A maximum frequency of 129 MHz is obtained for the modified design.
The Effect of Biomechanical Features on Classification of Dual-Task Gait

Early detection of Alzheimer’s disease and related disorders (ADRDs) has been a focus of research with the hope that early intervention may improve clinical outcomes. The manifestation of motor impairment in the early stages of ADRD has led to the inclusion of gait assessments including spatiotemporal parameters in clinical evaluations. This study aims to determine the effect of adding kinetic and kinematic gait features to the classification of different levels of cognitive load in healthy individuals. A dual-task paradigm was used to simulate cognitive impairment in 40 healthy adults, with single-task walking trials representing normal, healthy gait. The Paced Auditory Serial Addition Task (PASAT) was administered at two different interstimulus intervals (ISIs) representing two levels of cognitive load in dual-task gait. We predicted that a richer dataset would improve classification accuracy relative to spatiotemporal parameters. Repeated measures analysis of variance (ANOVA) showed significant changes in 15 different gait features across all three levels of cognitive load. We used three supervised machine learning algorithms to classify data points using a series of different gait feature sets with performance based on the area under the curve (AUC). Classification yielded 0.778 AUC across all three conditions (0.889 AUC Single versus Dual) using kinematic and spatiotemporal features compared to 0.724 AUC using spatiotemporal features only (0.792 AUC Single versus Dual). These data suggest that additional kinematic parameters improve classification performance. However, the benefit of measuring a wider set of parameters compared to their cost needs consideration. Further work will lead to a clinically viable ADRD detection classifier.
Analysis and Design of Substrate Integrated Waveguide Bandpass Filter for 5G Applications

This paper presents the analysis and design of a fourth-order substrate integrated waveguide (SIW) bandpass filter. The designed bandpass filter operates in the frequency range from 3.7 GHz to 3.98 GHz which covers the New C-band 5G network with fractional bandwidth (FBW) of 28% and centered at 3.84 GHz. The design procedures of bandpass characteristics are summarized by calculating the external quality factor and the coupling coefficients from the physical structure. The presented design is based on three direct inductive coupling between four arranged SIW cavity resonators and only one cross-coupling between the resonators at the input and output ports. This fourth-order bandpass filter is synthesized, designed, fabricated, and measured. Good agreement is achieved between the measured and the simulated results.
Energy-efficient full-duplex UAV relaying with trajectory optimization and power control in maritime communication environments

This paper solves an energy-efficient optimization problem of a fixed-wing unmanned aerial vehicle (UAV) assisted full-duplex mobile relaying in maritime communication environments. Taking the speed and the acceleration of the UAV and the information-causality constraints into consideration, the energy-efficiency of the system under investigation is maximized by jointly optimizing the UAV's trajectory and the individual transmit power levels of the source and the UAV relay nodes. The optimization problem is non-convex and thus cannot be solved directly. Therefore, it is decoupled into two subproblems. One sub-problem is for the transmit power control at the source and the UAV relay nodes, and the other aims at optimizing the UAV's flight trajectory. By using the Lagrangian dual and Dinkelbach methods, the two sub-problems are solved, leading to an iterative algorithm for the joint design of transmit power control and trajectory optimization. Computer simulations demonstrated that by conducting the proposed algorithm, the flight trajectory of the UAV and the individual transmit power levels of the nodes can be flexibly adjusted according to the system conditions, and the proposed algorithm can achieve significantly higher energy efficiency as compared with the other benchmark schemes.
Application of the Adaptive-Neuro Fuzzy Inference System (ANFIS) Method for Rapid Diagnosis COVID-19 Patient Cases

COVID-19 is a disease caused by the SARS-CoV-2 virus or often referred to as Corona Virus. In December 2019, this virus begin to spread from Wuhan, China to all over the world and was declared a pandemic. The virus attacks the respiratory tract so that sufferers have symptoms such as acute respiratory infection. In many cases, there are also patients with COVID-19 who do not have the following symptoms, making it difficult to determine the patient's COVID-19 status before a PCR test is performed. In this research, we try to do a rapid diagnosis with the final status of COVID-19 such as close contact, suspect, probable, and confirm, based on symptoms experienced by patients using the Adaptive Neuro-Fuzzy Inference System (ANFIS) method. ANFIS was chosen because ANFIS uses an artificial neural network concept that is suitable for use in patterned and complex calculations. ANFIS also has the basis of fuzzy logic that can map the expert and linguistic aspects of humans. Generated model from ANFIS training tested with entering symptoms patients data, then matched with COVID-19 status. Error calculation using MAE as an evaluation of the accuracy of this model. Evaluation is based on 10-fold cross validation. The experimental results obtained an accuracy of 82.39% with an MAE value of 0.1558 for training and 0.1903 for testing.