Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing optical power of GaN-based light-emitting diodes by nanopatterning on indium tin oxide with tunable fill factor using multiple-exposure nanosphere-lens lithography

In this study, the multiple-exposure nanosphere-lens lithography method utilizing the polystyrene nanospheres with focusing behavior is investigated and introduced to fabricate diverse photonic crystals (PCs) on indium tin oxide to enhance the optical output power of GaN-based light-emitting diode (LED). Simulated results indicate that the focused light intensity decreases with increasing tilted angle due to the shadow effect introduced by adjacent nanospheres. The fill factor of nanopattern is tunable by controlling tilted angles and exposure times. To attain quadruple PC without overlapping patterns, mathematical calculation model is used to define the optimum range of tilted angles. Angular emission patterns and three-dimensional finite-difference time domain simulated results indicate that the enhanced light extraction of PC LEDs results mainly from diffused scattering effects, and the diffraction effects of PC on light extracted efficiency increase with the increase of fill factor. Furthermore, it is confirmed that the multiple PC can extract more light from GaN into air than common PC with same period and fill factor.
Discovery of HCD3514 as a potent EGFR inhibitor against C797S mutation in vitro and in vivo

Osimertinib (AZD9291), a third-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKI), has significantly improved the survival of non-small cell lung cancer (NSCLC) patients with EGFRT790M mutation, the major mechanism of acquired resistance to first-generation EGFR TKI. However, resistance to AZD9291 arises eventually and EGFRC797S mutation was reported to be a major resistance mechanism. Thus, it is highly valuable to develop novel EGFR fourth-generation inhibitors targeting C797S mutation to override the acquired resistance. In this study, we identified HCD3514 as a novel EGFR fourth-generation inhibitors targeting C797S triple mutation. It strongly inhibited EGFRL858R/T790M/C797S and EGFR19del/T790M/C797S mutations with IC50 values of 1.0 and 2.0 nM, respectively. HCD3514 dose-dependently inhibited the activation of EGFR in both engineered BaF3 cells and tumor cells harboring EGFRC797S triple mutant and thus effectively suppressed the proliferation of the cells. Moreover, HCD3514 caused a dose-dependent increase of apoptosis in C797S triple mutant cells accompanied by increased levels of cleaved caspase-3 and cleaved PARP. Furthermore, HCD3514 induced tumor growth inhibition in EGFR19del/T790M/C797S xenograft model as a single oral agent by decreasing the activation of EGFR. In addition to EGFRC797S triple mutations, HCD3514 also potently and selectively inhibited EGFRT790M double mutations (L858R/T790M and 19del/T790M). Collectively, HCD3514 is a highly selective and potent EGFR inhibitor against EGFRC797S triple mutations as well as EGFRT790M double mutations and is confirmed potently anti-tumor activity in preclinical models.
Structural and practical identifiability of approximate metabolic network models

Parameter estimation from experimental data is a crucial problem in quantitative modeling of biochemical reaction networks. An especially important issue, raised by the complexity of the models and the challenging nature of the experimental data, is parameter identifiability. Despite several approaches proposed in the systems biology literature, no agreement exists on the analysis of structural and practical identifiability, and the relations among the two. In this paper we propose a mathematical framework for the analysis of identifiability of metabolic network models, establish basic results and methods for the structural and practical identifiability analysis of the class of so-called linlog models, and discuss the results on the basis of an artificial example.
Methods of Low-Density Lipoprotein-Cholesterol Measurement: Analytical and Clinical Applications

Among the five major classes of lipoprotein particles, low-density lipoprotein-cholesterol (LDL-C) is the primary lipoprotein risk factor for the development of cardiovascular diseases (CVD) through the promotion of atherosclerotic pathogenesis. Therefore, it is of paramount importance to accurately measure the plasma concentration of LDL-C using an appropriate method to examine the risk of CVD and determine the efficacy of therapeutic interventions to reduce the cholesterol level and examine the risk assessment strategy. At present, there is a wide variety of methods available for LDL-C measurement. In this review, we have outlined the commonly used methods of LDL-C measurement. These methods have been classified into non-automated analytical methods, calculation methods, and automated direct measurement of LDL-C. We have also described some recently proposed promising calculation methods which are being considered for clinical adoption. This current review could assist the clinicians to have a better understanding regarding the measurement techniques and comparative utilities of different methods of LDL-C measurement and guide them to select an appropriate method based on accuracy, turnaround time, and cost of test.
Nonparametric Function Estimation, Modeling, and Simulation

1. Historical Background 2. Some Approaches to Nonparametric Density Estimation 3. Maximum Likelihood Density Estimation 4. Maximum Penalized Likelihood Density Estimation 5. Discrete Maximum Penalized Likelihood Estimation 6. Nonparametric Density of Estimation in Higher Dimensions 7. Nonparametric Regression and Intensity Function Estimation 8. Model Building and Speculative Data Analysis Appendix I. An Introduction to Mathematical Optimization Theory Appendix II. Numerical Solution of Constrained Optimization Problems Appendix III. Optimization Algorithms for Noisy Problems Appendix IV. A Brief Primer in Simulation Index.
Polarization-independent bandwidth-variable tunable optical filter based on cholesteric liquid crystals

Right- and left-handed cholesteric liquid crystals (CLCs) with the same reflection band at the same temperature have been successfully fabricated by doping oversaturated chiral dopant S811 (left-handed) or R811 (right-handed) in the liquid crystal host BL006. The reflection band of both the right- and left-handed CLCs is blue-shifted with an increase in temperature. On the basis of this property, a polarization-independent tunable optical filter with variable bandwidth was demonstrated by combining left- and right-handed CLCs as a unit and configuring two CLC units in the reflection mode, in which the incident light is first reflected by one pair of CLCs and then reflected by the other one. The central wavelength can be tuned from 573 to 500 nm and the bandwidth can be varied from 10 to 70 nm.
The Mellin transforms and constant‐Q spectral analysis

It has been shown recently that constant‐Q spectral analysis constitutes one means for implementing a Fourier‐Mellin transform which is relevant in acoustical signal processing. This letter points out a further connection between constant‐Q spectral analysis and the Mellin transform: The latter is shown to be the basis for inverting the integral transforms representing both the ’’short‐time’’ and the ’’average power spectrum’’ resulting from any constant‐Q spectral analysis.
Nongyrotropy of heavy newborn ions at comet Grigg‐Skjellerup and corresponding instability

The existence of the nongyrotropic velocity distribution of newborn ions is a very important feature for comet Grigg-Skjellerup (P/G-S); this kind of distribution may be a reservoir of free energy and play a rather important role in the generation of plasma waves observed at P/G-S, especially close to the comet. In this paper we predict the distribution function of the newborn ion population, taking into account the source term for this population (implantation from neutrals) and the loss term (pickup, i.e., velocity diffusion leading to assimilation in the solar wind). We integrate the equation backward along the solar wind plasma flow line modified by the ion gyration motion using the observations of the Giotto spacecraft at P/G-S on July 10, 1992. We obtain the nongyrating zero-order nongyrotropic velocity distribution of the newborn ions which depends on the average pickup time of implanted ions and on the distance from the comet. The stability of the above equilibrium is then studied by adding linear perturbations and solving numerically the inferred dispersion equation. We show that this kind of distribution function triggers a left-handed instability which can be called “nongyrotropic ring instability” and which arises from the coupling between the two transverse electromagnetic modes and the longitudinal electrostatic mode.
Analysis by synthesis of electrolarynx speech

The ability to accurately synthesize electrolarynx (EL) speech may provide a basis for better understanding the acoustic deficits that contribute to its poor quality. Such information could also lead to the development of acoustic enhancement methods that would improve EL speech quality. This effort was initiated with an analysis‐by‐synthesis approach that used the Klatt formant synthesizer to study vowels at the end of utterances spoken by the same subjects both before and after laryngectomy (normal versus EL speech). The temporal and spectral features of the original speech waveforms were analyzed and the results were used to guide synthesis and to identify parameters for modification. EL speech consistently displayed utterance‐final fixed (mono) pitch and normal‐like falling amplitude across different vowels and subjects. Subsequent experiments demonstrated that it was possible to closely match the acoustic characteristics and perceptual quality of both normal and EL speech with synthesized replicas. I.
Estimate of Core Shroud Temperature by means of a CFD Model of Core Bypass

A power uprate of Forsmark’s Unit 3 from 109 % to 125 % will be implemented during the 2010 refuelling outage. This implies an increase in gamma heating of the core shroud which could lead to temperatures higher than the design thickness-mean temperature, 300 °C, according to ASME regulations. The CFX 5 model to estimate the core shroud temperature distribution consists of the core bypass, from the lower core support plate up to the core grid, and the upper plenum, limited from above by the core shroud cover with steam separator inlets. The conjugate heat transfer at the core shroud inner wall comprises gamma heating from the core, considered as volume distributed heat sources, and subcooled boiling of the bypass flow. The effect of subcooled boiling has been included by using the model by Kurul and Podowski. Using a conservative gamma heat source distribution, leads a maximum thickness-mean temperature that exceeds the temperature limit by approximately 4 °C. If a higher temperature limit is accepted, the ASME regulations are not fulfilled, but the consequence is a minor change in the design stress intensity value, Sm. Using a somewhat realistic gamma heat source distribution, the maximum thickness-mean temperature is well below the design temperature. Decreasing the core inlet temperature from 275.4 °C to 272.85 °C, i.e. the core inlet temperature used today, leads to no subcooled boiling at the core shroud inner wall and to a different location of the maximum temperature.
The soft X-ray diffuse background and the structure of the local interstellar medium

Data from five rocket flights of the Wisconsin soft X-ray sky survey, covering the southern galactic hemisphere with 6.5 deg spatial resolution between 0.1 and 2.0 keV, are presented as intensity maps. Analysis of the data in two energy bands below 0.3 keV is presented here. These low-energy X-rays appear to be emitted from a plasma of about one-million K, the bulk of which is closer to the sun than even the nearest 5 x 10 to the 19th/sq cm of neutral gas. No evidence is found for the presence of nearby small (radius of about 0.6 pc) clouds of cool neutral gas, as has been proposed by McKee and Ostriker (1977). The X-ray measurements are consistent with the small amounts of local neutral material inferred from Lyman-alpha absorption measurements.
fintech-kMC: Agent based simulations of financial platforms for design and testing of machine learning systems

We discuss our simulation tool, ﬁntech-kMC, which is de- signed to generate synthetic data for machine learning model development and testing. ﬁntech-kMC is an agent-based model driven by a kinetic Monte Carlo (a.k.a. continuous time Monte Carlo) engine which simulates the behaviour of cus- tomers using an online digital ﬁnancial platform. The tool provides an interpretable, reproducible, and realistic way of generating synthetic data which can be used to validate and test AI/ML models and pipelines to be used in real-world customer-facing ﬁnancial applications.
Probabilistic Genotype-Phenotype Maps Reveal Mutational Robustness of RNA Folding, Spin Glasses, and Quantum Circuits

Recent studies of genotype-phenotype (GP) maps have reported universally enhanced phenotypic robustness to genotype mutations, a feature essential to evolution. Virtually all of these studies make a simplifying assumption that each genotype maps deterministically to a single phenotype. Here, we introduce probabilistic genotype-phenotype (PrGP) maps, where each genotype maps to a vector of phenotype probabilities, as a more realistic framework for investigating robustness. We study three model systems to show that our generalized framework can handle uncertainty emerging from various physical sources: (1) thermal fluctuation in RNA folding, (2) external field disorder in spin glass ground state finding, and (3) superposition and entanglement in quantum circuits, which are realized experimentally on a 7-qubit IBM quantum computer. In all three cases, we observe a novel biphasic robustness scaling which is enhanced relative to random expectation for more frequent phenotypes and approaches random expectation for less frequent phenotypes.
Another look at Stein's method for Studentized nonlinear statistics with an application to U-statistics

We take another look at using Stein's method to establish uniform Berry-Esseen bounds for Studentized nonlinear statistics, highlighting variable censoring and an exponential randomized concentration inequality for a sum of censored variables as the essential tools to carry the arguments involved. As an important application, we prove a uniform Berry-Esseen bound for Studentized U-statistics with a kernel of any given degree.
A Probabilistic Framework for Visual Localization in Ambiguous Scenes

Visual localization allows autonomous robots to relocalize when losing track of their pose by matching their current observation with past ones. However, ambiguous scenes pose a challenge for such systems, as repetitive structures can be viewed from many distinct, equally likely camera poses, which means it is not sufficient to produce a single best pose hypothesis. In this work, we propose a probabilistic framework that for a given image predicts the arbitrarily shaped posterior distribution of its camera pose. We do this via a novel formulation of camera pose regression using variational inference, which allows sampling from the predicted distribution. Our method outperforms existing methods on localization in ambiguous scenes. We open-source our approach and share our recorded data sequence at github.com/efreidun/vapor.
Self-assembly of wires in acrylate monomer via nanoparticle dielectrophoresis

This study investigates morphology and bulk growth rate of wires using dielectrophoresis in non-aqueous suspensions of carbon black nanoparticles dispersed in a diacrylate monomer. Growing anisotropic structures in adhesives to enhance thermal or electrical conductivity is highly desirable for many applications including semiconductor packing. As in aqueous suspensions, the wire growth threshold and growth rate depend on applied voltage and electrode separation. While assembly rate depends less on applied frequency than other field aspects, the morphology of assembled wires transitions from linear to fractal morphology with increasing frequency. Additionally, the wire growth rate in non-aqueous suspensions is temperature dependent and exhibits an upper threshold temperature for wire assembly.
False Alarms Revealed in a Planet Search of TESS Light Curves

In this paper, we investigate the impact of false alarms on planet searches of TESS data by performing a search of a large number of stars. We examine the period distribution of transit-like signatures uncovered in a Box-Least Squares transit search of TESS light curves, and show significant pileups at periods related to instrumental and astrophysical noise sources. Signatures uncovered in a search of inverted light curves feature similar structures in the period distribution. Automated vetting methods will need to remove these excess detections, and light curve inversion appears to be a suitable method for simulating false alarms and designing new vetting metrics. Automated vetting will be a significant step toward making TESS data useful for demographic studies.
COMPUTER CONTROLLED PROBE DIAGNOSTIC SYSTEM AND APPLICATIONS IN A MAGNETIZED LABORATORY PLASMA

A computer controlled acquisition and evaluation system for electrostatic probes is presented. Fully automatic evaluation of the probe characteristics yields electron temperature, plasma potential, and density during the recording. Two‐dimensional probe positioning is accomplished with stepping motors. The system is characterized by 12‐bit probe current resolution and extremely wide probe voltage range (±130 V). It is tested in a collisionless argon magnetoplasma and compared to standard wave diagnostic techniques for magnetized plasmas, i.e., the resonance cone technique. The influence of the probe radius on the electron current in a magnetized plasma is studied experimentally. The two techniques yield identical density and temperature values within 20% accuracy and therefore show the reliable applicability of the automatic evaluation system.
Multi-Task Learning for Budbreak Prediction

Grapevine budbreak is a key phenological stage of seasonal development, which serves as a signal for the onset of active growth. This is also when grape plants are most vulnerable to damage from freezing temperatures. Hence, it is important for winegrowers to anticipate the day of budbreak occurrence to protect their vineyards from late spring frost events. This work investigates deep learning for budbreak prediction using data collected for multiple grape cultivars. While some cultivars have over 30 seasons of data others have as little as 4 seasons, which can adversely impact prediction accuracy. To address this issue, we investigate multi-task learning, which combines data across all cultivars to make predictions for individual cultivars. Our main result shows that several variants of multi-task learning are all able to significantly improve prediction accuracy compared to learning for each cultivar independently.
Dynamics of active particles with translational and rotational inertia

Inertial effects affecting both the translational and rotational dynamics are inherent to a broad range of active systems at the macroscopic scale. Thus, there is a pivotal need for proper models in the framework of active matter to correctly reproduce experimental results, hopefully achieving theoretical insights. For this purpose, we propose an inertial version of the active Ornstein–Uhlenbeck particle (AOUP) model accounting for particle mass (translational inertia) as well as its moment of inertia (rotational inertia) and derive the full expression for its steady-state properties. The inertial AOUP dynamics introduced in this paper is designed to capture the basic features of the well-established inertial active Brownian particle model, i.e. the persistence time of the active motion and the long-time diffusion coefficient. For a small or moderate rotational inertia, these two models predict similar dynamics at all timescales and, in general, our inertial AOUP model consistently yields the same trend upon changing the moment of inertia for various dynamical correlation functions.
Semantic match: Debugging feature attribution methods in XAI for healthcare

The recent spike in certified Artificial Intelligence (AI) tools for healthcare has renewed the debate around adoption of this technology. One thread of such debate concerns Explainable AI (XAI) and its promise to render AI devices more transparent and trustworthy. A few voices active in the medical AI space have expressed concerns on the reliability of Explainable AI techniques and especially feature attribution methods, questioning their use and inclusion in guidelines and standards. Despite valid concerns, we argue that existing criticism on the viability of post-hoc local explainability methods throws away the baby with the bathwater by generalizing a problem that is specific to image data. We begin by characterizing the problem as a lack of semantic match between explanations and human understanding. To understand when feature importance can be used reliably, we introduce a distinction between feature importance of low- and high-level features. We argue that for data types where low-level features come endowed with a clear semantics, such as tabular data like Electronic Health Records (EHRs), semantic match can be obtained, and thus feature attribution methods can still be employed in a meaningful and useful way. Finally, we sketch a procedure to test whether semantic match has been achieved.
Peristaltic Flow of Herschel-Bulkley Fluid in an Elastic Tube with Slip at Porous Walls

A mathematical model for the impact of slip velocity on peristaltic transport of blood flow has been investigated by utilizing the Herschel-Bulkley model in a flexible tube. The closed-form solutions are obtained for velocity, plug flow velocity, and volume flux. It is noticed that the impact of yield stress, amplitude ratio, Darcy number, velocity slip parameter, elastic parameters and fluid behavior index plays a vital role in controlling the flux in an elastic tube. The outcomes acquired from the flow quantities reveal that, the volume flux in a flexible tube decreases with an increase in the porous parameter and it increases with an increase in the slip parameter. Further, the results of Newtonian, Bingham plastic and Power-law models have been presented graphically and analysed
Dynamics of Argon Gas Bubbles Rising in Liquid Steel in the Presence of Transverse Magnetic Field

Bubbly flows are present in various industrial processes including metallurgical processes in which gas bubbles are injected at the bottom of bulk liquid metal to stir the liquid metal and homogenize the metal. Understanding the motion of such bubbles is essential, as it has been shown that bubble flotation can remove inclusions. In this work, we have numerically studied three-dimensional dynamics of a pair of inline Argon bubbles rising in molten steel under the influence of a transverse magnetic field. We have explored the effects of two transverse magnetic field strengths (Bx = 0 and 0.2 T). The bubbles' motion and transient rise velocities are compared under different magnetic fields. The shape deformations and path of the bubbles are discussed. The flow structures behind the bubbles are analyzed. We found that structures are more organized and elongated under a magnetic field, whereas it is complex and intertwined when the magnetic field is not included. We have used a geometry construction-based volume of fluid (VOF) method to track interface, maintain mass balance and estimate the interface curvature. Additionally, we have incorporated a Sharp Surface Force Method (SSF) for surface tension forces. The algorithm is able to minimize the spurious velocities.