Miracle-dz/UniFAR-FTUs huggingface dataset:
Numerical computation of ultrasonic wave propagation in concrete using the elastodynamic finite integration technique (EFIT)

Concrete is a strongly heterogeneous solid including aggregates, cracks and porosity. It represents an important but also very challenging object for ultrasonic non-destructive testing methods. The elastic wave propagation in this material consists of a complex mixture of multiple mode conversion and multiple scattering which results in a "diffusive" energy transport. In order to investigate the effect of aggregates, porosity and reinforcement on the applicability and reliability of different testing methods it is useful to model the ultrasonic wave propagation and scattering process explicitly in the time-domain. The elastodynamic finite integration technique represents a stable and efficient numerical scheme to model ultrasonic wave propagation in elastic solids. In the present paper the two- and three-dimensional EFIT code is used to calculate ultrasonic wave propagation and scattering in various concrete specimens modeling pulse-echo, impact-echo and acoustic emission testing methods. The numerical simulations are presented by means of time-domain signals and time-domain wavefield snapshots.
Range-dependent path-loss model in residential areas for the VHF and UHF bands

Implementation digital fixed wireless systems, such as digital FM, high definition TV (HDTV) and wireless local loop (WILL), require a high level of accuracy of propagation prediction in the VHF and UHF bands. This paper presents a theoretical model that predicts the average path loss from an elevated base station to subscriber antennas at rooftop level in residential areas for frequencies in the approximate band from 30 MHz up to 3 GHz. In addition, the model gives the standard deviation of the path loss at the average rooftop level and the height gain for subscriber antennas above or below the average rooftop level. The approach used here generalizes that of Walfisch and Bertoni (1988), who developed a theoretical model of UHF propagation in urban environments. The rows of buildings in residential areas are modeled as a series of dielectric screens having random heights and uniform spacing. Propagation takes place over and through the dielectric screens, and ground reflections are included. The fields at the plane of each screen are obtained in terms of repeated numerical evaluation of the physical optics (PO) integral. Results obtained for the variation of the range dependence on frequency and row spacing are consistent with those previously found for high frequencies by assuming the screens to be totally absorbing. Using the results at different heights of receiving antenna allows computation of the height gain. The predictions are in agreement with the International Telecommunication Union Radio Communication (ITRU-R) recommendations in the VHF and UHF bands.
Causal Models for Patterns of Nonresponse

Abstract The problem of missing data for categorical variables is examined from the perspective of modeling the mechanisms of nonresponse. Log-linear causal models, as formulated by Goodman, are studied for the relationship of the survey variables to response; under some conditions several such models are estimable from the observed data. For nested patterns of nonresponse, a specific causal model represents exactly the assumption of ignorable response. Most causal models, however, imply nonignorable response mechanisms and yield alternative estimates for the distribution of the survey variables.
Long-term Development Perspectives of Resilient Companies

Resilience, as a characteristic of a business organization to prepare, adapt, withstand and survive economic distress periods is essential for a continuous operation and also contributes to a balanced macroeconomic state of regions. It is not evident whether being resilient once or several times during the lifetime of a business organization in which aspect can contribute to long-term survival and success. In this study annual revenue information – gathered from more than 26,000 Hungarian companies from processing industry – is utilized in order to construct an empirical resilience metric and to investigate long-term behaviour of companies that had successfully withstood turbulent economic periods. Based on matched-pair analysis our results showed that resilient organizations despite being successful in the short-run statistically tend to lag behind in terms of performance in the long-run.
DeepEnsemble: A Novel Brain Wave Classification in MI-BCI using Ensemble of Deep Learners

Brain computer interfaces (BCIs) are rapidly growing in neurorehabilitation to realize communication for consumers with disabilities. This work proposes a classification approach for the motor imagery BCI electroencephalogram (EEG) signals using the recent advances in deep learning methods. The proposed DeepEnsemble method combines different deep learning models in an ensemble learning with soft voting. More specifically, multi-layer perceptron, vision transformer, convolutional neural network and its integration with distributed gradient boosting methods are explored separately and combined in an ensemble learning model. The proposed method is evaluated on a publicly available dataset. It is shown that the proposed ensemble learning method significantly improves the classification accuracy of the EEG signals across various subjects as compared to some of the existing methods.
On the critical fluctuations in superconductors

The field-theoretical gauge model for a superconductor, generalized to an n/2-component complex order parameter is considered. The question of the order of the phase transition occurring in this model is discussed. Previous renormalization-group calculations suggest that in such a model a fluctuation-induced first-order phase transition occurs. We re-examine previously obtained expressions for the renormalization-group functions in the two-loop approximation in three dimensions. Special attention is being paid to the fact that the corresponding series might be the asymptotic ones and therefore have zero radius of convergence. We discuss the possible ways of analytical continuation of the series obtained. Comparing results obtained by `direct' calculations with those obtained by Pade analysis and the Pade - Borel resummation technique, the conjecture is made that in the model under consideration there still exists a possibility for a second-order phase transition.
Examining Subject-Dependent and Subject-Independent Human Affect Inference from Limited Video Data

Continuous human affect estimation from video data entails modelling the dynamic emotional state from a sequence of facial images. Though multiple affective video databases exist, they are limited in terms of data and dy-namic annotations, as assigning continuous affective labels to video data is subjective, onerous and tedious. While studies have established the existence of signature facial expressions corresponding to the basic categorical emotions, individual differences in emoting facial expressions nevertheless exist; factoring out these idiosyncrasies is critical for effective emotion inference. This work explores continuous human affect recognition using AFEW-VA, an ‘in-the-wild’ video dataset with limited data, employing subject-independent (SI) and subject-dependent (SD) settings. The SI setting involves the use of training and test sets with mutually exclusive subjects, while training and test samples corresponding to the same subject can occur in the SD setting. A novel, dynamically-weighted loss function is employed with a Convolutional Neural Network (CNN)-Long Short- Term Memory (LSTM) architecture to optimise dynamic affect prediction. Superior prediction is achieved in the SD setting, as compared to the SI counterpart.
Ultrasound inverse scattering determination of speed of sound, density, and absorption

This paper presents a method of determining three intrinsic mechanical parameters of an unknown object in the framework of ultrasound inverse scattering tomography. Those parameters are the speed of sound, density, and absorption whose values are given as the solution of an inhomogeneous Helmholtz wave equation. Computer simulations are carried out using the sinc basis moment method and Newton-Raphson method in an measurement configuration where the object is insonified by an incident plane wave over 360/spl deg/ and the scattered field is collected by detectors arranged in a rectangular fashion around it. Two distinct frequencies are used to separate each parameter of three Gaussian objects that are either overlapped at the same location or separately positioned from each other. The density turns out to be rather difficult to reconstruct as compared with the other two parameters.
Charge-state distributions of chlorine ions interacting with cold gas and with fully ionized plasma

Charge transfer of 4.3 MeV/u chlorine ions passing through a discharge plasma target is used as a probe to determine the plasma density and the ratio of impurities inside the plasma column. Charge-state distributions of 2 MeV/u chlorine ions passing through the plasma are then measured and compared to corresponding measurements in the cold gas. Stopping power measurements are also performed in both cases.
Optimization of Multiphase Aircraft Trajectories Using Hybrid Optimal Control

An approach to optimize multiphase trajectories of commercial transport aircraft is presented. The approach is based on the theory of hybrid optimal control, and it is applied to the case of minimum-fuel trajectories. The multiphase trajectories are composed of three types of phases, climb, cruise, and descent, in a given sequence. In each phase, the optimal control is scalar and of the bang–singular–bang type, and the optimal path is formed by a singular arc and two minimum/maximum-control arcs joining the singular arc with the initial and final switching points. An indirect numerical method is developed, which takes into account the structure of the solution directly in the algorithm and exploits the singular character of the problem. In the analysis, the effects of horizontal winds are taken into account; general along-track wind and crosswind profiles are considered, dependent both on altitude and along-track position. The optimal trajectories are computed for a model of a Boeing 767-300ER performing .
TUSR-Net: Triple Unfolding Single Image Dehazing With Self-Regularization and Dual Feature to Pixel Attention

Single image dehazing is a challenging and ill-posed problem due to severe information degeneration of images captured in hazy conditions. Remarkable progresses have been achieved by deep-learning based image dehazing methods, where residual learning is commonly used to separate the hazy image into clear and haze components. However, the nature of low similarity between haze and clear components is commonly neglected, while the lack of constraint of contrastive peculiarity between the two components always restricts the performance of these approaches. To deal with these problems, we propose an end-to-end self-regularized network (TUSR-Net) which exploits the contrastive peculiarity of different components of the hazy image, i.e, self-regularization (SR). In specific, the hazy image is separated into clear and hazy components and constraint between different image components, i.e., self-regularization, is leveraged to pull the recovered clear image closer to groundtruth, which largely promotes the performance of image dehazing. Meanwhile, an effective triple unfolding framework combined with dual feature to pixel attention is proposed to intensify and fuse the intermediate information in feature, channel and pixel levels, respectively, thus features with better representational ability can be obtained. Our TUSR-Net achieves better trade-off between performance and parameter size with weight-sharing strategy and is much more flexible. Experiments on various benchmarking datasets demonstrate the superiority of our TUSR-Net over state-of-the-art single image dehazing methods.
Analytic, co-analytic and projective sets from Brouwer's intuitionistic perspective

We study projective subsets of Baire space from Brouwer's intuitionistic point of view, using his Thesis on Bars and his continuity axioms. We first study analytic sets; these are the projections of the closed subsets of Baire space. We consider a number of examples and discover a fine structure in the class of the analytic sets that fail to be positively Borel. A subset of Baire space is strictly analytic if it coincides with the range of a continuous function from Baire space to itself. We prove separation and boundedness theorems for strictly analytic sets. Co-analytic sets are the co-projections of the open subsets of Baire space. We show different ways to prove that some co-analytic sets are not analytic and that some analytic sets are not co-analytic. We consider the set of the codes of the closed and located subsets of Baire space that are almost-countable as an example of a set that is a projection of a co-analytic set. We bring to light the collapse of the projective hierarchy: every (positively) projective set coincides with the projection of a co-analytic set.
Simulation of container filling process with two inlets by improved smoothed particle hydrodynamics (SPH) method

In this article, an improved smoothed particle hydrodynamics (SPH) method is proposed to simulate the filling process with two inlets. Improvements are achieved by deriving a corrected kernel gradient of SPH and a density re-initialisation. In addition, a new treatment of solid wall boundaries is presented. Thus, the improved SPH method has higher accuracy and better stability, and conserves both linear and angular momentums. The validity of the new boundary treatment is shown by simulating the spin-down problem. The bench tests are also presented to demonstrate the performance of the improved SPH method. Then the filling process with a single inlet is simulated to show the ability to capture complex-free surface of the proposed method. Finally, the filling process with two inlets is numerically investigated. The numerical results show that the filling patterns are affected significantly by Reynolds number, aspect ratio of the container and the location of the inlets.
On the motion of a rigid block, tethered at one corner, under harmonic forcing

A rigid block, tethered at one corner, is subjected to harmonic forcing. The motion is shown to be equivalent to that of the inverted pendulum impacting one of a pair of asymmetrically placed side-walls. The dynamics of the problem contain subharmonic responses, multiple solutions, period-doubling bifurcations, etc. Stability boundaries are given for a wide range of parameters and orbits are shown to be possible for a large range of forcing amplitudes. Some orbits are possible at forcing amplitudes larger than those in the untethered case. A period- and impact-doubling sequence is shown explicitly for the first time, using digital simulations. Evidence is offered for the existence of more than one type of multiple-impact solution. Large amplitude subharmonic orbits are found.
Zero Touch Realization of Pervasive Artificial Intelligence as a Service in 6G Networks

The vision of the upcoming 6G technologies, characterized by ultra-dense networks, low latency, and fast data rate, is to support pervasive artificial intelligence (PAI) using zero touch solutions enabling self-X (e.g., self-configuration, self-monitoring, and self-healing) services. However, the research on 6G is still in its infancy, and only the first steps have been taken to conceptualize its design, investigate its implementation, and plan for use cases. Toward this end, academia and industry communities have gradually shifted from theoretical studies of AI distribution to real-world deployment and standardization. Still, designing an end-to-end framework that systematizes the AI distribution by allowing easier access to the service using a third-party application assisted by zero touch service provisioning has not been well explored. In this context, we introduce a novel platform architecture to deploy a zero touch PAI as a service (PAlaaS) in 6G networks supported by a blockchain-based smart system. This platform aims to standardize the PAI at all levels of the architecture and unify the interfaces in order to facilitate service deployment across application and infrastructure domains, relieve users' worries about cost, security, and resource allocation, and at the same time respect the 6G's stringent performance requirements. As a proof of concept, we present a federated-learning-as-a-service use case where we evaluate the ability of our proposed system to self-optimize and self-adapt to the dynamics of 6G networks in addition to minimizing the users' perceived costs.
The accessibility property of expansive geodesic flows without conjugate points

Abstract Let (M,g) be a compact, smooth Riemannian manifold without conjugate points whose geodesic flow is expansive. We show that the geodesic flow of (M,g) has the accessibility property, namely, given two points θ1, θ2 in the unit tangent bundle, there exists a continuous path joining θ1, θ2 formed by the union of a finite number of continuous curves, each of which is contained either in a strong stable set or in a strong unstable set of the dynamics. Since expansive geodesic flows of compact surfaces have no conjugate points, the accessibility property holds for every two-dimensional expansive geodesic flow.
Unified Approach for Estimation of State of Charge and Remaining Useful Life of Li-Ion batteries using Deep Learning

Lithium-Ion Batteries (LIBs) are among the most used batteries in consumer electronics or electric vehicles due to various properties ranging from higher charge density to longer charge-discharge cycles. As the batteries will be the primary power source in these applications, maintaining the battery health and knowing the accurate State of Charge (SoC) is crucial. The model-based approaches may not produce accurate results in all scenarios because of the non-linear relation between the Remaining Useful Life (RUL) and SoC of LIBs. To overcome this, data-driven deep learning approaches are used. Long Short-Term Memory (LSTM) is one of the widely used algorithms to estimate SoC or RUL independently using different datasets. This paper presents a unified framework to predict both SoC and RUL using the same LSTM architecture and the dataset. The Center for Advanced Life Cycle Engineering (CALCE) battery datasets are used to train and test the proposed system. The validation accuracy obtained for both SoC and RUL is around 97%. It is observed that the results obtained are on par or slightly improved when compared to the existing algorithms that use a similar dataset to predict either SoC or RUL independently. This unified approach addresses any dependencies in data for practical applications.
How might educational research into children’s ideas about light be of use to teachers?

This paper offers a synthesis of research evidence around teaching light to primary and secondary school pupils as part of the Institute of Physics Promoting and Interpreting Physics Education Research (PIPER) project. Conceptual change literature describes many of the difficulties young people can have understanding the phenomenon of light, and this knowledge can be useful in the classroom. Pupil–teacher dialogue is used to illustrate some of the pedagogical challenges teachers face in this topic. This paper also highlights the range of influences on pupils from everyday life and from the classroom, with a view to promoting teacher awareness of conceptual change research evidence.
Theoretical Study on the Influence of the Position of the Bearing Plate Under the Horizontal Load on the Failure State of the NT-CEP Pile

Through the simulation analysis of ANSYS finite element analysis software, seven groups of model piles are designed using the bearing plate position as the main parameter to determine the influence of different NT-CEP pile plate positions (new-type concrete expanded plate pile) on the horizontal bearing capacity and the failure state of soil around the pile under horizontal load. Through the analysis of displacement nephogram and stress nephogram, the displacement values of pile top and pile bottom under different loads are extracted, and the load displacement curve and pile top bottom displacement curve are drawn. The corresponding conclusions are drawn from the analysis of the simulation results, thereby providing a theoretical basis for the further research on the design of NT-CEP pile under horizontal bearing capacity and promoting the development and application of NT-CEP pile in practical projects.
Novel Uniplanar Electromagnetic Bandgap Structure for High Gain Antenna and Filter Designs

New uniplanar electromagnetic bandgap (EBG) element is designed in this work for the superstrate of highly directive planar antenna and microwave filter design. The layout of the EBG is conceived of four arms spiral resonators (FASRs) and four corner square patches as a part of interdigital capacitor (IDC). The unit cell has dimension of only 3 mm, and the structure is engineered on a grounded dielectric substrate Taconic TLC-32 of thickness 0.79 mm. The designed EBG exhibits a wide bandgap property between the frequencies of 9.6-12.48 GHz ( 26.1 %). This work focuses on two different applications of the EBG – one as a superstrate of the microstrip antenna and the second as EBG loaded microstrip filter. The EBG loaded patch shows gain enhancement of 7.1 dB and bandwidth (BW) improvement of 330 MHz as compared to the reference antenna. A novel EBG based wideband filter is proposed for the C-band application. The passband BW of the filter covers the frequencies of 4.76-7.29 GHz (42 %) having insertion loss (IL) in the structure is below 1.2 dB over the operating range.
Modelling of the effective permittivity of anisotropic filament structures

Generating a rigorous, description of the effective permittivity of anisotropic media requires an appropriate consideration of its microstructural geometry and orientation. In this study, we use anisotropic geometrical models made of filaments along with electromagnetic wave propagation calculations to investigate the effect of filament orientation on the effective permittivity. As a result, we have identified a correlation tensor parameter that, in conjunction with the filament orientation tensor provides efficient and reliable estimates of the effective permittivity of filamentary structures with solid volume fractions below 10 %.
Cochlear Implants and the Right to Language: Ethical Considerations, the Ideal Situation, and Practical Measures Toward Reaching the Ideal

Cochlear implants (CIs) in small children who do not yet have a firm footing in first language acquisition are an on-going experiment with human subjects, in the sense that the risks involved have not been properly identified, much less assessed, due to the failure to focus on the biology of language and its role in first language acquisition. Too often, the developmental cognitive milestones of the deaf child and the right to language are not considered, and we risk contributing to cases of linguistic deprivation with all the ensuing consequences. We propose an immediate remedy: to teach deaf children a sign language, along with training in speech and speech-reading. For many families, such as those that live far from a Deaf community, as in a rural situation, this presents practical problems, which we address.