Miracle-dz/UniFAR-FTUs huggingface dataset:
MasQU: Finite Differences on Masked Irregular Stokes Q,U Grids

The detection of B-mode polarization in the CMB is one of the most important outstanding tests of inflationary cosmology. One of the necessary steps for extracting polarization information in the CMB is reducing contamination from so-called "ambiguous modes" on a masked sky, which contain leakage from the larger E-mode signal. This can be achieved by utilising derivative operators on the real-space Stokes Q and U parameters. This paper presents an algorithm and a software package to perform this procedure on the nearly full sky, i.e., with projects such as the Planck Surveyor and future satellites in mind; in particular, the package can perform finite differences on masked, irregular grids and is applied to a semi-regular spherical pixellization, the HEALPix grid. The formalism reduces to the known finite-difference solutions in the case of a regular grid. We quantify full-sky improvements on the possible bounds on the CMB B-mode signal. We find that in the specific case of E and B-mode separation, there exists a "pole problem" in our formalism which produces signal contamination at very low multipoles l. Several solutions to the "pole problem" are presented; one proposed solution facilitates a calculation of a general Gaussian quadrature scheme, which finds application in calculating accurate harmonic coefficients on the HEALPix sphere. Nevertheless, on a masked sphere the software represents a considerable reduction in B-mode noise from limited sky coverage.
Robust control of mode transition for a single-motor full hybrid electric vehicle

The single-motor full hybrid electric vehicle tends to suffer drivability and excessive clutch wear issues during the mode transition from electric driving to hybrid driving because the single motor must simultaneously propel the vehicle and divert torque via clutch engagement to start the engine. In this study, a novel four-phase compound robust control method consisting of the feedforward–feedback technology and the robust compensation approach is proposed to address these problems during the mode transition. The whole mode transition process was divided into four consecutive phases according to the operating status of engine and clutch. In each phase, a nominal controller based on the feedforward–linear quadratic regulator feedback technology is first designed for the nominal linear model to guarantee a desired control performance. And then, the nonlinear uncertain part of the engine/clutch torque and the vehicle resistant torque as well as the model parameter uncertainties are all considered as equivalent disturbances, and a robust compensator is introduced to suppress the effect of equivalent disturbances. Simulation is performed to validate the effectiveness of the proposed control method. The results show that the four-phase compound robust control method can achieve better vehicle drivability and minimize clutch wear during the mode transition from electric driving to hybrid driving even in the presence of various torque disturbances and parameter uncertainties.
CDLX: An Efficient novel approach for COVİD detection lung x-rays throw transfer learning based on State of the art deep learning image classification models

When compared to the general population, lung cancer patients have a higher incidence of COVID-19 infection, pulmonary problems, and poorer survival results. As a reference for prioritising cancer care issues during the epidemic, the world's main professional organisations issued new recommendations for the diagnosis, treatment, and follow-up of lung cancer patients. In today's world, we are fighting one of the greatest pandemics in human history, known as COVID-2019, which is caused by a coronavirus. The patient can be treated promptly if the infection is detected early (before it enters the lower respiratory tract). To observe ground-glass opacity in the chest X-ray due to fibrosis in the lungs once the virus has reached the lungs. Artificial intelligence techniques can be utilised to detect the presence and degree of illness based on the major discrepancies between X-ray images of an infected and non-infected person. For this study, I employed feature extraction from Transfer Learning, which entails importing a pre-trained CNN model, such as Distributed Deep Convolutional VGGNet or Distributed Deep Convolutional with ResNet Model, and changing the last layer to meet my needs.
Implementation of the next-generation Gas Cherenkov Detector at the National Ignition Facility

The newest Gas Cherenkov Detector (GCD-3) diagnostic has completed its Phase I commissioning/milestone at the National Ignition Facility (NIF). GCD-3 was fielded for several years at the Omega Laser Facility in its initial configuration, before being moved to the NIF. Installation at the NIF involved optimization of GCD-3 for the higher background environment and designing a new insertion carrier assembly. GCD-3 serves as the initial phase towards the implementation of the “Super GCD” (SGCD) at the NIF. During this phase of development GCD-3 took measurements from a re-entrant well, 3.9 meters from target chamber center (TCC). Plans to insert GCD-3 within 20 cm of TCC with a Target and Diagnostic Manipulator (TANDM) will be discussed. Data was collected using a Photomultiplier Tube (PMT) in combination with a Mach-Zehnder based recording system. These measurements were used to aid in shielding analysis, validate MCNP models, and fuel design efforts for the SGCD. Findings from the initial data will be covered extensively, including an in-depth look into sources of background and possible mitigation strategies. Ongoing development of phase two, the addition of an ultra-high bandwidth Pulse Dilatation Photomultiplier Tube (PD-PMT), will also be presented.
A FREQUENCY RECONFIGURABLE MEANDERED SLOT CUT RECTANGULAR PATCH ANTENNA USING PIN DIODES

A frequency reconfigurable patch antenna is proposed. The antenna has a rectangular patch with two meandered slots. It can be switched between four bands using two PIN diodes by altering current distribution across the slot edges. The overall dimension of the antenna patch is 11.51mm × 8.37 mm and fabricated on an FR4 substrate. The design is investigated by simulation and measurement, and the result includes S11 parameters, radiation patterns, measured directivity and gain. With different combinations of PIN diode biasing conditions, the antenna can be set to 6.80 GHz, 7.34 GHz, 7.80 GHz and 8.18 GHz, which collectively covers a continuous frequency range of 1.80 GHz (−10 dB bandwidth). The antenna also shows consistent radiation patterns at all the reconfigured frequency bands with an average beam width of about 75◦. In the accessible frequency range, an average gain of 5.14 dBi and low level of cross polarizations are also recorded. A good agreement between measured and simulated results validates the presented concept of frequency reconfiguration.
Visual media as a tool to acquire soft skills — cross-disciplinary teaching-learning project SUFUvet

Structured Abstract Purpose – SUFUvet is a cross-disciplinary teaching-learning project designed to adapt students’ soft skills and track usability and the concrete surplus value of work techniques in the field of visual media design. Design/methodology/approach – For SUFUvet, a collaboration between the Institute of Food Hygiene/University of Leipzig and the Media Center/Technische Universität Dresden was initiated. Bachelor students of media informatics generate 3D visualisations in the framework of SCRUM: Undergraduate veterinary students issue instructions in order to create an e-learning class. During the project, questionnaires, group discussions, and feedback methods are used to detect changes in selected soft skills. Originality/value – This design is meant to increase knowledge and employability by adapting student’s media, communication, and project management competences. Using SCRUM appears to be a new approach, not only in the field of programming, but for media production as well. Additionally, it offers an interdisciplinary work environment, which is rare but considered fruitful within university studies. Practical implications – The outcomes of the application are a 3D-visualised meat inspection e-learning class for veterinary students plus a documentation of SCRUM as a framework for visual media design. It is seen as an experiment for future applications in a variety of cross-disciplinary learning and media design cases. Acknowledgements – This research was financially facilitated by “Lehrpraxis im Transfer (LiT)”, a Saxon joint project funded by the German Federal Ministry of Education and Research.
A Milroy Disease Family Caused by FLT4 Gene Mutation of c.2774 T>A

Background: Milroy disease (MD) is a rare, autosomal dominant disorder. Mutations in the Fms-related tyrosine kinase 4 (FLT4) gene cause the symptoms of this disease. In this report, we investigated the mutations in a large Chinese family with MD.Methods: We conducted Sanger sequencing of exons 17–26 of the FLT4 (NM_182925.4) gene. The primers were as follows: forward, 5' CTTCATCAGCGTCGAGTGG 3' and reverse, 5' ATTATGGGCGGGTTCCTT 3'. The amplification system is as follows: 2×Biotech Power PCR Mix, 10 µl; forward primer, 0.8 µl (10 µM); reverse primer, 0.8 µl (10 µM); DNA template, 1 µl (50 ng/µl); and ddH2O, 13.4 µl. The mutation was evaluated with MutationTaster, SIFT and PolyPhen.Results: A heterozygous substitution was detected in all patients but not in any healthy controls (FLT4 gene: c.2774 T>A, p.V925E). The mutation was predicted to be pathogenic.Conclusions: In this report, we described a large family with MD caused by a missense mutation of the FLT4 gene (c.2774 T>A, p.V925E).
Failure of Slopes and Embankments Under Static and Seismic Loading

The stability of slopes and embankments under the influence of static and seismic loads has been the subject of study for many researchers. This paper presents the mechanisms and causes of landslides as well as the forms of failure of slopes and embankments under static and seismic loading, with examples of failures from both Greek and international space. There is also mention to measures to protect and stabilize landslides, categories of slope stability analysis, and methods of seismic impact analysis . What follows is the determination of tolerable movements based on the caused damage on natural slopes, dams and embankments and an attempt is made to connect them with the vulnerability curves that are one of the key elements of stochastic seismic hazard . Particular importance is given to the statistical parameters of the mechanical characteristics of the sloping soil mass and to the simulation of random fields necessary for solving complex geotechnical works. Finally, we compare the simulation and description of random fields and the L.A. S. method is observed to be the most accurate of all simulation methods. The L.A. S. algorithm in conjunction with finite difference models can demonstrate the large fluctuations in the factor of safety values and the permanent seismic displacements of the slopes under the effect of seismic charges whose time histories are known.
Rational Approximations in Quantum Chromodynamics

We discuss some topics concerning rational approximations in Quantum Chromodynamics, especially those related with the mathematical theory of Pad\'e Approximants. We focus on two kind of problems: the first one related with meromorphic functions (inspired by the Large-Nc limit in QCD) where we explore the Minimal Hadronic Approximation through the extraction of Low-Energy Constants and Condensate parameters of a two-point Green's function; and the second one related with meromorphic functions of Stieltjes-type where we present a critical analysis to a unitarization process applied to the Linear Sigma Model and also and application of the Pad\'e Theory to the vacuum polarization function of a heavy quark. We also show the ability of these approximations when working with experimental data. Along these line, we make special emphasis on the reliability of that theory to control on systematic errors.
T HE I MPACT ON G ROWTH OF E ASING R EGULATIONS IN U PSTREAM S ECTORS

The need to find ways to boost GDP growth is particularly important in the recovery from the current recession. The implementation of structural reforms aiming at decreasing anti-competitive regulations may be one of them. The multifactor productivity (MFP) gains obtained from such reforms could improve significantly potential output growth, thereby also facilitating the adjustment of public finances, which have suffered from the crisis and the ensuing recovery plans.We document in this paper some simulations, which are based on estimates of a “neoShumpeterian” model and tend to show that important MFP growth gains could be obtained in developed countries by adopting best regulation practices in “upstream sectors”, i.e., sectors that are chief providers of intermediate inputs to the economy.
Un algorithme en ligne pour la detection de nouveauté dans un flux de documents

Cet article traite d’une tâche de recherche d’information qui est la detection et le suivi d’evenements dans un flot de documents textuels. Avec la multiplication des flux et des sources d’information, les enjeux de cette tâche, notamment pour la veille technologique, economique, etc. sont considerables. Nous proposons d’ameliorer des al- gorithmes utilises pour la detection d’evenement en combinant classification automatique et selection de variables dans des algorithmes incrementaux. Les experiences realisees sur un corpus de grande taille montrent que les modeles proposes obtiennent de bonnes performances pour un faible temps de calcul.
Sample-Based Minimax Approach and Its Application to Linear Quadratic Optimization under Uncertainty

During the recent period of time, several techniques have been elaborated to reduce the sensitivity of the standard procedures of estimation, optimization and control to the initial uncertainty in the models under consideration, i.e., to make them robust with respect to the shortage of initial information about the model parameters [1]–[13]. Most of the results are based on the minimax approach, which means that the optimal in a minimax sense strategy minimizes the upper bound of the risk functional that is calculated over some known set of the model parameters containing their true values with acceptable reliability. In this paper, we study the following optimization problem:
DYNAMIC MODEL OF USING POWERED LITHIUM-ION BATTERIES FOR EV / HEV APPLICATIONS

: The work presents a simple and easy to use dynamic model for the work of the lithium-ion battery type LiFeYPO4. The dynamics model of charging and discharging of the battery is validated experimentally with a traction battery used in EV / HEV. An interesting feature of this model is the simplicity for extracting of the dynamic parameters of technical specifications of the battery. Only two points on the curve for the discharge of the battery in the steady state are required to obtain the necessary parameters. The model of the battery is included in the simulation using the LabJack U12 in the programming environment LabVIEW2012 for EV / HEV applications. The results show that the model can accurately represent the dynamic behavior of the battery during use of EV / HEV vehicles.
Relaxor ferroelectricity in strained epitaxial SrTiO3 thin films on DyScO3 substrates

The ferroelectric properties of 500A thick strained, epitaxial SrTiO3 films grown on DyScO3 substrates by reactive molecular-beam epitaxy are reported. Despite the near 1% biaxial tensile strain, the x-ray rocking curve full widths at half maximum in ω are as narrow as 7arcsec (0.002°). The films show a frequency-dependent permittivity maximum near 250K that is well fit by the Vogel-Fulcher equation. A clear polarization hysteresis is observed below the permittivity maximum, with an in-plane remanent polarization of 10μC∕cm2 at 77K. The high Tmax is consistent with the biaxial tensile strain state, while the superimposed relaxor behavior is likely due to defects.
A Bayesian approach for inverse modeling, data assimilation, and conditional simulation of spatial random fields

This paper addresses the inverse problem in spatially variable fields such as hydraulic conductivity in groundwater aquifers or rainfall intensity in hydrology. Common to all these problems is the existence of a complex pattern of spatial variability of the target variables and observations, the multiple sources of data available for characterizing the fields, the complex relations between the observed and target variables and the multiple scales and frequencies of the observations. The method of anchored distributions (MAD) that we propose here is a general Bayesian method of inverse modeling of spatial random fields that addresses this complexity. The central elements of MAD are a modular classification of all relevant data and a new concept called “anchors. ” Data types are classified by the way they relate to the target variable, as either local or nonlocal and as either direct or indirect. Anchors are devices for localization of data: they are used to convert nonlocal, indirect data into local distributions of the target variables. The target of the inversion is the derivation of the joint distribution of the anchors and structural parameters, conditional to all measurements, regardless of scale or frequency of measurement. The structural parameters describe large‐scale trends of the target variable fields, whereas the anchors capture local inhomogeneities. Following inversion, the joint distribution of anchors and structural parameters is used for generating random fields of the target variable(s) that are conditioned on the nonlocal, indirect data through their anchor representation. We demonstrate MAD through a detailed case study that assimilates point measurements of the conductivity with head measurements from natural gradient flow. The resulting statistical distributions of the parameters are non‐Gaussian. Similarly, the moments of the estimates of the hydraulic head are non‐Gaussian. We provide an extended discussion of MAD vis à vis other inversion methods, including maximum likelihood and maximum a posteriori with an emphasis on the differences between MAD and the pilot points method.
A Four Layer Bayesian Network for Product Model Based Information Mining

Summary Business and engineering knowledge in AEC/FM is captured mainly implicitly in project and corporate document repositories. Even with the increasing integration of model-based systems with project information spaces, a large percentage of the information exchange will further on rely on isolated and rather poorly structured text documents. In this paper we propose an approach enabling the use of product model data as a primary source of engineering knowledge to support information externalisation from relevant construction documents, to provide for domain-specific information retrieval, and to help in re-organising and re-contextualising documents in accordance to the user’s discipline-specific tasks and information needs. Suggested is a retrieval and mining framework combining methods for analysing text documents, filtering product models and reasoning on Bayesian networks to explicitly represent the content of text repositories in personalisable semantic content networks. We describe the proposed basic network that can be realised on short-term using minimal product model information as well as various extensions towards a full-fledged added value integration of document-based and model-based information.
Coordinate-Reduced Steepest Gradient Descent for Computed Tomography Parallel Reconstruction

—The inversion of linear systems is fundamental in Computed Tomography (CT) reconstruction. Computational challenges arise when trying to invert large linear systems, as limited computing resources mean that only part of the system can be kept in computer memory at any one time. In linear tomographic inversion problems such as x-ray tomography, even a standard scan can produce millions of individual measurements and the reconstruction of x-ray attenuation proﬁles typically requires the estimation of a million attenuation coefﬁcients. To deal with the large data sets encountered in real applications and to efﬁciently utilise modern graphics processing unit (GPU) based computing architectures, combinations of iterative reconstruction algorithms and parallel computing schemes are increasingly applied. Whilst different parallel methods have been proposed, individual computations currently need to access either the entire set of observations or estimated x-ray absorptions, which can be prohibitive in many realistic applications. We present a fully parallelizable CT image reconstruction algorithm where each computation node works on arbitrary partial subsets of the data and the reconstructed volume. We further develop a non-homogeneously randomised selection criteria which guarantees that sub-matrices of the system matrix are selected more fre-quently if they are dense, thus maximising information ﬂow through the algorithm. We compare our algorithm with block alternating direction method of multipliers (block ADMM) and show that our method is signiﬁcantly faster for CT reconstruction.
Speech Enhancement using Multiple Transducers

In this thesis, three methods of speech enhancement techniques are investigated with applications in extreme noise environments. Various beamforming techniques are evaluated for their performance characteristics in terms of signal to (distant) noise ratio and tolerance to design imperfections. Two suitable designs are identified with contrasting performance characteristics — the second order differential array, with excellent noise rejection but poor robustness; and a least squares design, with adequate noise rejection and good robustness. Adaptive filters are introduced in the context of a simple noise canceller and later a post-processor for a dual beamformer system. Modifications to the least mean squares (LMS) filter are introduced to tolerate cross-talk between microphones or beamformer outputs. An adaptive filter based post-processor beamforming system is designed and evaluated using a simulation involving speech in noisy environments. The beamforming methods developed are combined with the modified LMS adaptive filter to further reduce noise (if possible) based on correlations between noise signals in a beamformer directed to the talker and a complementary beamformer (nullformer) directed away from the talker. This system shows small, but not insignificant, improvements in noise reduction over purely beamforming based methods. Blind source separation is introduced briefly as a potential future method for enhancing speech in noisy environments. The FastICA algorithm is evaluated on existing data sets and found to perform similarly to the post-processing system developed in this thesis. Future avenues of research in this field are highlighted.