Miracle-dz/UniFAR-FTUs huggingface dataset:
Cardiorespiratory Fitness in the Prevention and Management of Cardiovascular Disease

Cardiovascular disease (CVD) is the leading cause of death among adults in the U.S. and elsewhere. Variation in the presence, severity, and control of major modifiable risk factors accounts for much of the variation in CVD rates worldwide. Cardiorespiratory fitness (CRF) reflects the integration of ventilation, circulation, and metabolism for the delivery and utilization of oxygen in support of dynamic aerobic physical activity. The gold standard measure of CRF is maximal oxygen uptake. Because the primary factor underlying differences in this measure between individuals is maximal cardiac output, it can serve as a clinical indicator of cardiac function. Higher CRF is associated with favorable levels of major CVD risk factors, lower prevalence and severity of subclinical atherosclerosis, and lower risks of developing both primary and secondary clinical CVD events. The beneficial associations between CRF and CVD are seen in women and men, older and younger adults, in those with multiple coexisting risk factors or prior diagnosis of CVD. Exercise training and regular physical activity of at least moderate intensities and volumes improves CRF in adults, and improvements in CRF are associated with lower risks of subsequent CVD and mortality. Routine assessment of CRF in primary care settings could enhance individual-level CVD risk assessment and thereby guide implementation of appropriate measures to prevent future clinical events.
An Optimized-LSTM and RGB-D Sensor-Based Human Gait Trajectory Generator for Bipedal Robot Walking

Bipedal robots are gaining significant attention due to their wide range of applications in various fields such as manufacturing, transportation, rehabilitation, and many others. However, mimicking human-like walking in bipedal robots is a challenging task as human walking represents a complex learning process that requires the coordination of the human brain motor cell with its leg muscles. Besides, maintaining walking stability in bipedal robots is another significant task that has not been fully explored. To address the challenges mentioned above and improve the performance of bipedal robot walk, this work proposes a long short-term memory (LSTM)-based human gait trajectory generator. A low-cost and markerless motion capture sensor system (Microsoft Kinect V2) is used to capture human gait data during a treadmill walk. The captured data is then analyzed and utilized to extract the significant features using the proposed model. Furthermore, the selection of significant hyperparameter(s) has also been focused on achieving the optimal performance for the proposed model using the Rao-3 metaheuristic optimization algorithm (optimized-LSTM). The stability performance of the generated gait trajectories is verified through standard evaluation measures like cyclograms and phase plots. The gait trajectories obtained through the proposed model are also validated on the HOAP-2 robot simulator.
‘Had I gone into the office, they would have caught it a little bit sooner’: narrative problematics in U.S. pandemic birth stories

ABSTRACT Individuals who gave birth during the COVID-19 pandemic experienced an increased risk for premature births, stillbirths, depression, and lower access to care. Their stories provide valuable information that can inform clinical care, particularly due to loss of in-person support resulting from visitor restrictions in hospitals. Grounded in a theory of narrative problematics, we explored how elicited birth narratives were affected by COVID-19 and how stories can be used as material evidence to inform healthcare systems. We facilitated seven focus group discussions with 65 women from 19 states who had given birth between March and July 2020. Three themes emerged from our qualitative thematic analysis: (1) navigating disrupted access to healthcare; (2) experiencing loss of co-construction of birth experience; and (3) recognizing fissures in the mask-wearing master narrative. Practical implications for improving healthcare include developing spaces for individuals to process birth stories for cathartic benefit due to significant disruption, improving hospital policies about in-person support to avoid loss of co-construction of experience, and centering hospitals and the providers that work within them as audiences for interventions around preventive measures during a disease outbreak.
Photogrammetric 3D Measurements and Visualization of Flow in Hydrodynamic Tunnel

Flow visualization is an important mean for studying flow processes in aerodynamics and hydrodynamics. It allows obtaining qualitative valuable information about flow behaviour, that is needed for understanding of aerodynamic performance of an aircraft, especially in critical conditions. With growing advances in 3D optical measuring techniques, accurate 3D registration and measurements of fast developing processes became possible. The paper addresses the problem of accurate metric 3D reconstruction of flow in hydrodynamic tunnel that is necessary for studying aerodynamic process in aircraft icing conditions. It presents the techniques developed for optical measurement system calibration and for accurate flow 3D registration, and the results of laboratory flow 3D reconstruction and visualization. Experimental evaluation of the developed techniques in laboratory hydrodynamic tunnel demonstrated high accuracy of 3D measurements and readiness for applying in aircraft icing study.
Droop Controlled Microgrid With DSTATCOM for Reactive Power Compensation and Power Quality Improvement

A droop controlled microgrid with distribution static compensator (DSTATCOM) is developed to improve the power quality in this study. Due to the reactive power/voltage Q-V droop characteristic and the existence of the unbalanced, linear inductive and nonlinear loads, the power quality problems, including the voltage drop, unbalanced currents, lagging power factor (PF) and current harmonics, are very serious in the islanded microgrid. Moreover, owing to the instantaneous power following into or out of the DC-link capacitor of the DSTATCOM under load variation, the performance of the DSTATCOM for power quality improvement is seriously degenerated. Hence, to effectively improve the power quality of the droop controlled microgrid and the transient response of the DC-link voltage of the DSTATCOM under load variation, an online trained polynomial petri fuzzy neural network (PPFNN) controller is firstly proposed as the DC-link voltage controller to supersede the conventional proportional-integral (PI) controller in the DSTATCOM. The network structure and the online learning strategy of the proposed PPFNN are detailedly derived. Finally, the effectiveness of the DSTATCOM using the proposed PPFNN controller to improve the unbalanced currents, the total harmonic distortion (THD) reduction of the current and to compensate the reactive power for the voltage support and PF correction in the droop controlled microgrid is certified.
Genetic Associations of Visfatin Polymorphisms with EGFR Status and Clinicopathologic Characteristics in Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is the most common histologic type of lung cancer. Mutations of the epidermal growth factor receptor (EGFR) gene are among the most common genetic alterations in LUAD and are the targets of EGFR tyrosine kinase inhibitors. The enzyme visfatin is involved in the generation of the oxidized form of nicotinamide adenine dinucleotide (NAD+) and regulation of intracellular adenosine triphosphate (ATP), critical processes in cancer cell survival and growth. This study explored the relationship between visfatin single nucleotide polymorphisms (SNPs) with EGFR status and the clinicopathologic development of LUAD in a cohort of 277 Taiwanese men and women with LUAD. Allelic discrimination of four visfatin SNPs rs11977021, rs61330082, rs2110385 and rs4730153 was determined using a TaqMan Allelic Discrimination assay. We observed higher prevalence rates of advanced (T3/T4) tumors and distant metastases in EGFR wild-type patients carrying the rs11977021 CT + TT and rs61330082 GA + AA genotypes, respectively, compared with patients carrying the CC and GG genotypes. EGFR wild-type patients carrying the rs11977021 CT + TT genotypes were also more likely to develop severe (stage III/IV) malignancy compared with patients carrying the CC genotype. An analysis that included all patients found that the association persisted between the rs11977021 CT + TT and rs61330082 GA + AA genotypes and the development of T3/T4 tumors compared with patients carrying the rs11977021 CC and rs61330082 GG genotypes. In conclusion, these data indicate that visfatin SNPs may help to predict tumor staging in LUAD, especially in patients with EGFR wild-type status.
Covariance Properties of Reflection Equation Algebras

The reflection equations (RE) are aconsistent extension of the Ya·ng-Baxter equations (YBE) with an addition of one element, the so·called reflection matrix or K-matrix. For example, they describe the conditions for factorizable scattering on a half line just like the YBE give the conditions for factorizable scattering on an entire line. The YBE were generalized to define quadratic algebras, 'Yang· Baxter algebras' (YBA) which were used intensively for the discussion of quantum groups. Similarly, the RE define quadratic algebras, 'the reflection equation algebras' (REA), which enjoy various remarkable properties both new and inherited from the YBA. Here we focus on the various properties of the REA, in particular, the quantum group comodule properties, generation of a series of new solutions by composing known solutions, the extended REA and the central elements, etc.
On the origin of order from random two-body interactions

We investigate the origin of order in the low-lying spectra of many-body systems with random two-body interactions. Contrary to the common belief our study based both on analytical as well as on numerical arguments shows that these are the higher $J$-sectors whose ground states are more orderly than the ones in the J=0-sector. A predominance of J=0 ground states turns out to be the result of putting on together states with different characteristic energy scales from different $J$-sectors.
Algebraic Models of Hadron Structure

The algebraic treatment of baryons is extended to strange resonances. Within this framework we study a collective string-like model in which the radial excitations are interpreted as rotations and vibrations of the strings. We derive a mass formula and closed expressions for strong and electromagnetic decay widths and use these to analyze the available experimental data. PACS numbers: 14.20. Jn, 13.30. Eg, 13.40. Hq, 03.65. Fd Annals of Physics (N.Y.), in press
Suppression of electronic correlations by chemical pressure from FeSe to FeS

In iron-based superconductors, the interplay between nematicity, magnetism, and superconductivity is still not fully understood. Experimentally, the isoelectronic series FeSe${}_{1-x}$S${}_{x}$ provides an ideal playground to tune different electronic ground states and to selectively investigate their mutual interplay. Here, the authors use high-resolution angle-resolved photoemission spectroscopy to compare the electronic structure of different single crystals in the tetragonal phase. They find that in the absence of nematicity and magnetism, the substantial reduction of electronic correlations correlates with the decrease of the superconducting transition temperature from FeSe to FeS.
Improved measurement of the positive muon anomalous magnetic moment

A new measurement of the positive muon's anomalous magnetic moment has been made at the Brookhaven Alternating Gradient Synchrotron using the direct injection of polarized muons into the superferric storage ring. The angular frequency difference omega (a) between the angular spin precession frequency omega (s) and the angular orbital frequency omega (c) is measured as well as the free proton MMR frequency omega (p). These determine R = omega (a)/omega (p) = 3.707 201(19) x 10(-3). With mu (mu)/mu (p) = 3.183 345 39(10) this gives a(mu+) = 11 659 191(59) x 10-(10) (+/-5 ppm), in good agreement with the previous CERN and BNL measurements for mu (+) and mu (-), and with the standard model prediction.
System Modeling and Artificial Neural Network (ANN) Design for Lateral and Longitudinal of F-16

Today, the classical control methods are still widely used because of their excellent performance in a working environment with conditions of geo-graphical distance. They are suitable for functions of the system: more flexible operating system, easy to perform, less unwanted ricks occur, the efficiency of controlling a system better. Besides the traditional control methods, the author has applied more modern and smarter algorithms such as artificial intelligence to control a system on the ground or a system moving in the air. In this paper, artificial neural network (ANN) is applied for a flight model to demonstrate its effectiveness in all cases. ANN in this article to show off its amazing application for flying devices. This is a useful method because it is highly secure. Simulation is done by Matlab.
4th Order LC-Based Sigma Delta Modulators

Due to the characteristic of narrow band conversion around a central radio frequency, the Sigma Delta Modulator (ΣΔM) based on LC resonators is a suitable option for use in Software-Defined Radio (SDR). However, some aspects of the topologies described in the state-of-the-art, such as noise and nonlinear sources, affect the performance of ΣΔM. This paper presents the design methodology of three high-order LC-Based single-block Sigma Delta Modulators. The method is based on the equivalence between continuous time and discrete time loop gain using a Finite Impulse Response Digital-to-Analog Converter (FIRDAC) through a numerical approach to defining the coefficients. The continuous bandpass LC ΣΔM simulations are performed at a center frequency of 432 MHz and a sampling frequency of 1.72 GHz. To the proposed modulators a maximum Signal-to-Noise Ratio (SNR) of 51.39 dB, 48.48 dB, and 46.50 dB in a 4 MHz bandwidth was achieved to respectively 4th Order Gm-LC ΣΔM, 4th Order Magnetically Coupled ΣΔM and 4th Order Capacitively Coupled ΣΔM.
CCD Parallaxes for 309 Late-type Dwarfs and Subdwarfs

New, updated, and/or revised CCD parallaxes determined with the Strand Astrometric Reflector at the Naval Observatory Flagstaff Station are presented. Included are results for 309 late-type dwarf and subdwarf stars observed over the 30+ years that the program operated. For 124 of the stars, parallax determinations from other investigators have already appeared in the literature and we compare the different results. Also included here are new or updated VI photometry on the Johnson–Kron-Cousins system for all but a few of the faintest targets. Together with 2MASS JHKs near-infrared photometry, a sample of absolute magnitude versus color and color versus color diagrams are constructed. Because large proper motion was a prime criterion for targeting the stars, the majority turn out to be either M-type subdwarfs or late M-type dwarfs. The sample also includes 50 dwarf or subdwarf L-type stars, and four T dwarfs. Possible halo subdwarfs are identified in the sample based on tangential velocity, subluminosity, and spectral type. Residuals from the solutions for parallax and proper motion for several stars show evidence of astrometric perturbations.
Basics of Control of the Bow Shock Wave, Drag and Lift Forces, and Stability in a Steady Supersonic Flow Past an AD Body Using Permanently Operating Thermally Stratified Energy Deposition

A new method of high-speed flow control using permanently operating thermally stratified energy deposition is presented. The paper focuses on the analysis of the dependence of the characteristics of a steady supersonic flow and an aerodynamic (AD) body on the temperature values in the layers of a stratified source and the possibility of making the transition from one steady flow mode to another by changing the temperature in the layers. A detailed visualization of the dynamics of the fields of density, pressure, temperature, and local Mach number is presented during the controlled establishment of steady flow modes. Multiple generation of the Richtmyer–Meshkov instability is shown. The sharp peaks accompanying the development of the Richtmyer–Meshkov instabilities were obtained, which remain in the steady flow mode established under the action of a stratified energy source. Basic approaches for controlling the bow shock wave, drag and lift (pitch) forces (at zero angle of attack), and the stability in a steady supersonic flow past an AD body using permanently operating thermally stratified energy source were developed. The possibility of initiating and damping self-sustained flow pulsations as well as the formation of a steady flow with oppositely directed constantly acting lift forces due to temperature changes in the layers of a thermally stratified energy source is shown.
Machine Learning with Enormous “Synthetic” Data Sets: Predicting Glass Transition Temperature of Polyimides Using Graph Convolutional Neural Networks

In the present work, we address the problem of utilizing machine learning (ML) methods to predict the thermal properties of polymers by establishing “structure–property” relationships. Having focused on a particular class of heterocyclic polymers, namely polyimides (PIs), we developed a graph convolutional neural network (GCNN), being one of the most promising tools for working with big data, to predict the PI glass transition temperature Tg as an example of the fundamental property of polymers. To train the GCNN, we propose an original methodology based on using a “transfer learning” approach with an enormous “synthetic” data set for pretraining and a small experimental data set for its fine-tuning. The “synthetic” data set contains more than 6 million combinatorically generated repeating units of PIs and theoretical values of their Tg values calculated using the well-established Askadskii’s quantitative structure–property relationship (QSPR) computational scheme. Additionally, an experimental data set for 214 PIs was also collected from the literature for training, fine-tuning, and validation of the GCNN. Both “synthetic” and experimental data sets are included into a PolyAskInG database (Polymer Askadskii’s Intelligent Gateway). By using the PolyAskInG database, we developed GCNN which allows estimation of Tg of PI with a mean absolute error (MAE) of about 20 K, which is 1.5 times lower than in the case of Askadskii QSPR analysis (33 K). To prove the efficiency and usability of the proposed GCNN architecture and training methodology for predicting polymer properties, we also employed “transfer learning” to develop alternative GCNN pretrained on proxy-characteristics taken from the popular quantum-chemical QM9 database for small compounds and fine-tuned on an experimental Tg values data set from PolyAskInG database. The obtained results indicate that pretraining of GCNN on the “synthetic” polymer data set provides MAE which is almost twice as low as that in the case of using the QM9 data set in the pretraining stage (∼41 K). Furthermore, we address the questions associated with the influence of the differences in the size of the experimental and “synthetic” data sets (so-called “reality gap” problem), as well as their chemical composition on the training quality. Our results state the overall priority of using polymer data sets for developing deep neural networks, and GCNN in particular, for efficient prediction of polymer properties. Moreover, our work opens up a challenge for the theoretically supported generation of large “synthetic” data sets of polymer properties for the training of the complex ML models. The proposed methodology is rather versatile and may be generalized for predicting other properties of different polymers and copolymers synthesized through the polycondensation reaction.
Searching for Exosatellites Orbiting L and T Dwarfs: Connecting Planet Formation to Moon Formation and Finding New Temperate Worlds

L-type and T-type dwarfs span the boundaries between main-sequence stars, brown dwarfs, and planetary-mass objects. For these reasons, L and T dwarfs are the perfect laboratories for exploring the relationship between planet formation and moon formation, and evidence suggests they may be swarming with close-in rocky satellites, though none have been found to date. The discovery of satellites orbiting L or T dwarfs will have transformative implications for the nature of planets, moons and even life in the Universe. These transiting satellites will be prime targets for characterization with NASA's James Webb Space Telescope. In this white paper, we discuss the scientific motivations behind searching for transiting satellites orbiting L and T dwarfs and argue that robotizing current 1-to-2-meter US optical/infrared (O/IR) facilities and equipping them with recently developed low-cost infrared imagers will enable these discoveries in the next decade. Furthermore, robotizing the 1-to-2-meter O/IR fleet is highly synergistic with rapid follow-up of transient and multi-messenger events.
Lack of high-mass pre-stellar cores in the starless MDCs of NGC 6334

Context. The formation of high-mass stars remains unknown in many aspects. There are two competing families of models to explain the formation of high-mass stars. On the one hand, quasi-static models predict the existence of high-mass pre-stellar cores sustained by a high degree of turbulence. On the other hand, competitive accretion models predict that high-mass proto-stellar cores evolve from low or intermediate mass proto-stellar cores in dynamic environments. Aims. The aim of the present work is to bring observational constraints at the scale of high-mass cores (~0.03 pc). Methods. We targeted with ALMA and MOPRA a sample of nine starless massive dense cores (MDCs) discovered in a recent Herschel/HOBYS study. Their mass and size (~110 M⊙ and r = 0.1 pc, respectively) are similar to the initial conditions used in the quasi-static family of models explaining for the formation of high-mass stars. We present ALMA 1.4 mm continuum observations that resolve the Jeans length (λJeans ~ 0.03 pc) and that are sensitive to the Jeans mass (MJeans ~ 0.65 M⊙) in the nine starless MDCs, together with ALMA-12CO(2–1) emission line observations. We also present HCO+(1–0), H13CO+(1–0) and N2H+(1–0) molecular lines from the MOPRA telescope for eight of the nine MDCs. Results. The nine starless MDCs have the mass reservoir to form high-mass stars according to the criteria by Baldeschi et al. (2017). Three of the starless MDCs are subvirialized with αvir ~ 0.35, and four MDCs show sign of collapse from their molecular emission lines. ALMA observations show very little fragmentation within the MDCs. Only two of the starless MDCs host compact continuum sources, whose fluxes correspond to <3 M⊙ fragments. Therefore, the mass reservoir of the MDCs has not yet been accreted onto compact objects, and most of the emission is filtered out by the interferometer. Conclusions. These observations do not support the quasi-static models for high-mass star formation since no high-mass pre-stellar core is found in NGC 6334. The competitive accretion models, on the other hand, predict a level of fragmentation much higher than what we observe.