Miracle-dz/UniFAR-FTUs huggingface dataset:
Searching for Stellar and Planetary Emission in Large Field-of-view Radio Sky Surveys

Detection of low-frequency (≤1.4 GHz) radio emission from stellar and planetary systems can lead to new insights into stellar activity, extrasolar space weather, and planetary magnetic fields. In this work, we investigate three large field-of-view surveys at 74 MHz, 150 MHz, and 1.4 GHz, as well as a myriad of multiwavelength ancillary data, to search for radio emission from about 2600 stellar objects, including about 800 exoplanetary systems, 600 nearby low-mass stars, and 1200 young stellar objects located in the Taurus and Upper Scorpius star-forming regions. The selected sample encompasses stellar spectral types from B to L and distances between 5 and 300 pc. We report the redetection of five stars at 1.4 GHz, one of which also shows emission at 150 MHz. Four of these are low- and intermediate-mass young stars, and one is the evolved star α Sco. We also observe radio emission at the position of a young brown dwarf at 1.4 GHz and 150 MHz. However, due to the large astrometric uncertainty of radio observations, a follow-up study at higher angular resolution would be required to confirm whether the observed emission originates from the brown dwarf itself or a background object. Notably, all of the selected radio sources are located in nearby star-forming regions. Furthermore, we use image stacking and statistical methods to derive upper limits on the average quiescent radio luminosity of the families of objects under investigation. These analyses provide observational constraints for large-scale searches for current and ongoing low-frequency radio emissions from stars and planets.
Insights into the decomposition of zirconium acetylacetonate using synchrotron radiation: Routes to the formation of volatile Zr-intermediates

The thermal decomposition of Zr(acac)4 is studied in a SiC-microreactor on the micro-second time scale. By utilizing synchrotron radiation and photoelectron photoion coincidence spectroscopy, six important zirconium intermediates, as for instance Zr(C5H7O2)2(C5H6O2), and Zr(C5H6O2)2, are identified in the gas phase for the first time. The adiabatic ionization thresholds of intermediately formed zirconium species are estimated and the main products of their thermal decomposition, acetylacetone, acetylallene and acetone are characterized unambiguously and isomer-selectively. Based on all detected intermediates, we deduce the predominant pyrolysis pathways of the precursor in the temperature range from 400 to 900 K. Our findings are complemented by numerical simulations of the flow field in the microreactor, which show that the choice of dilution gas significantly influences the temperature profile and residence times in the microreactor, such that helium provides a more uniform flow field than argon and should preferentially be used. Using a soft ionization method coupled to velocity map imaging (VMI), leads to valuable insights in the thermal decomposition of Zr(C5H7O2)4, used in the synthesis of functional nanomaterials and ceramic coatings. Thanks to the use of a microreactor, important gas
Path graphs

To render higher quality images from the samples generated by path tracing with a low sample count, we propose a novel path reuse approach that processes a fixed collection of paths to iteratively refine and improve radiance estimates throughout the scene. Our method operates on a path graph consisting of the union of the traced paths with additional neighbor edges inserted among clustered nearby vertices. Our approach refines the initial noisy radiance estimates via an aggregation operator, treating vertices within clusters as independent sampling techniques that can be combined using MIS. In a novel step, we also introduce a propagation operator to forward the refined estimates along the paths to successive bounces. We apply the aggregation and propagation operations to the graph iteratively, progressively refining the radiance values, converging to fixed-point radiance estimates with lower variance than the original ones. We also introduce a decorrelation (final gather) step, which uses information already in the graph and is cheap to compute, allowing us to combine the method with standard denoisers. Our approach is lightweight, in the sense that it can be easily plugged into any standard path tracer and neural final image denoiser. Furthermore, it is independent of scene complexity, as the graph size only depends on image resolution and average path depth. We demonstrate that our technique leads to realistic rendering results starting from as low as 1 path per pixel, even in complex indoor scenes dominated by multi-bounce indirect illumination.
Tunable Encrypted Deduplication with Attack-resilient Key Management

Conventional encrypted deduplication approaches retain the deduplication capability on duplicate chunks after encryption by always deriving the key for encryption/decryption from the chunk content, but such a deterministic nature causes information leakage due to frequency analysis. We present TED, a tunable encrypted deduplication primitive that provides a tunable mechanism for balancing the tradeoff between storage efficiency and data confidentiality. The core idea of TED is that its key derivation is based on not only the chunk content but also the number of duplicate chunk copies, such that duplicate chunks are encrypted by distinct keys in a controlled manner. In particular, TED allows users to configure a storage blowup factor, under which the information leakage quantified by an information-theoretic measure is minimized for any input workload. In addition, we extend TED with a distributed key management architecture and propose two attack-resilient key generation schemes that trade between performance and fault tolerance. We implement an encrypted deduplication prototype TEDStore to realize TED in networked environments. Evaluation on real-world file system snapshots shows that TED effectively balances the tradeoff between storage efficiency and data confidentiality, with small performance overhead.
Calibrated Click-Through Auctions

We analyze the optimal information design in a click-through auction with stochastic click-through rates and known valuations per click. The auctioneer takes as given the auction rule of the click-through auction, namely the generalized second-price auction. Yet, the auctioneer can design the information flow regarding the click-through rates among the bidders. We require that the information structure to be calibrated in the learning sense. With this constraint, the auction needs to rank the ads by a product of the value and a calibrated prediction of the click-through rates. The task of designing an optimal information structure is thus reduced to the task of designing an optimal calibrated prediction. We show that in a symmetric setting with uncertainty about the click-through rates, the optimal information structure attains both social efficiency and surplus extraction. The optimal information structure requires private (rather than public) signals to the bidders. It also requires correlated (rather than independent) signals, even when the underlying uncertainty regarding the click-through rates is independent. Beyond symmetric settings, we show that the optimal information structure requires partial information disclosure, and achieves only partial surplus extraction.
Automatic Music Transcription and Instrument Transposition with Differentiable Rendering

Automatic music transcription aims to extract a musical score from a given audio signal. Conventional machine learning frameworks usually address this task by relying solely on error back-propagation from annotated MIDI data, without consideration for acoustic similarities. In this study, we complement the onset and frames prediction objective with an acoustic distance, through differentiable rendering of the estimated piano-roll and approximate reconstruction of the analyzed signal. We apply our method to piano and show that this added reconstruction error improves the performance achieved with the usual supervised transcription loss. Moreover, using solely this acoustic criterion allows fully unsupervised training and results outperforming classical techniques. Finally, our method also enables performing automatic instrument transposition by using audio samples of a different instrument from the original sound source when reconstructing the input signal.
Chloroquine or Hydroxychloroquine and/or Azithromycin

Both chloroquine and hydroxychloroquine increase the endosomal pH, which inhibits fusion between SARS-CoV-2 and the host cell membrane.1 Chloroquine inhibits glycosylation of the cellular angiotensin-converting enzyme 2 (ACE2) receptor, which may interfere with the binding of SARS-CoV to the cell receptor.2 In vitro studies have suggested that both chloroquine and hydroxychloroquine may block the transport of SARS-CoV-2 from early endosomes to endolysosomes, possibly preventing the release of the viral genome.3 Both chloroquine and hydroxychloroquine also have immunomodulatory effects, which have been hypothesized to be another potential mechanism of action for the treatment of COVID-19. Azithromycin has antiviral and anti-inflammatory properties. When used in combination with hydroxychloroquine, it has been shown to have a synergistic effect on SARS-CoV-2 in vitro and in molecular modeling studies.4,5 However, despite demonstrating antiviral activity in some in vitro systems, neither hydroxychloroquine plus azithromycin nor hydroxychloroquine alone reduced upper or lower respiratory tract viral loads or demonstrated clinical efficacy in a rhesus macaque model.6
Electric Vehicle Battery Charger Using Bridgeless Landsman Converter

Nowadays people prefer electric vehicles more than the other Internal Combustion Engine vehicles because of the advantages such as less pollution, lower fuel and maintenance costs and better power factor improvement. The design will have AC source configured with modified bridgeless landsman converter, and this converter will boost up the regulating voltage in which the output voltage is twice that of the corresponding input voltage, which will fed to the push pull converter. The push pull converter will buck the voltage, and it is used to charge up the battery of the vehicle, and it controls the DC link voltage and maintains the unity power factor. Because of this, current ripple will be low, and the harmonic distortion will get reduced. Also, the efficiency can be maintained in a sufficient manner. The transformer rating will be reduced, and the total cost will be less, and this prototype is useful for maintaining the sustainable environment for all the cases.
CD 4 + FoxP 3 + CD 73 + regulatory T cell promotes cardiac healing 1 post-myocardial infarction 2 Running title : Zhuang et al . ; CD 73 + Treg promotes cardiac healing 3

1 Department of Cardiovascular Surgery, Shanghai East Hospital, Tongji University School of Medicine, 8 Shanghai, 200120, China 9 2 Research Center for Translational Medicine, Shanghai East Hospital, Tongji University School of 10 Medicine, Shanghai, 200120, China 11 3 Institute of Integrated Traditional Chinese and Western Medicine for Cardiovascular Chronic 12 Diseases, Tongji University School of Medicine, Shanghai, 200120, China 13 4 Shanghai Heart Failure Research Center, Shanghai, 200120, China 14 5 Department of Cardiology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, 15 200120, China 16 6 Department of Immunology and Microbiology, Shanghai Institute of Immunology, Shanghai Jiao 17 Tong University School of Medicine, Shanghai, China 18 7 Department of Child Internal Medicine, Shanghai Children’s Medical Center, Shanghai Jiaotong 19 University, Shanghai 200127, China 20 8 Translational Medical Center for Stem Cell Therapy, Shanghai East Hospital, Tongji University, 21 Shanghai 200120, China 22 9 Shanghai Institute of Stem Cell Research and Clinical Translation, Shanghai 200120, China 23 24 * Dr. Zhuang and Dr. Meng contributed equally to this work 25 # To whom correspondence should be addressed: 26 Xiaohui Zhou, M.D., Ph.D. 27 Research Center for Translational Medicine, 28 Shanghai East Hospital, Tongji University School of Medicine 29 150 Jimo Rd, Pudong, Shanghai, 200120, China 30 ORCID: 0000-0002-9354-3789 31 Tel:86-21-61569884 32 E-mail: zxh100@tongji.edu. cn 33 34 Zhongmin Liu, M.D., Ph.D. 35 Research Center for Translational Medicine, 36 Department of Cardiovascular Surgery, 37 Shanghai East Hospital, Tongji University School of Medicine 38 150 Jimo Rd, Pudong, Shanghai, 200120, China 39 E-mail: liu.zhongmin@tongji.edu. cn 40 41
Application of PLS-SEM in analyzing Mathematical Statistics II course online learning

The impact of the COVID-19 pandemic on the education sector includes the learning system that cannot be conducted offline. With the change in the learning system, it is necessary to analyze whether the implemented online learning system can run well and provide the expected results. The level of student satisfaction is one of the factors that can be used to analyze online learning. This study analyzed the student satisfaction level with online learning for the Mathematical Statistics II course at the Mathematics Department, Andalas University, related to the quality of lecture services and the level of student understanding. Data analysis was performed using causal a model, namely Partial Least Square Structural Equation Modeling (PLSSEM). By using The PLS-SEM, it is known that there is a significant and positive effect between service quality on student satisfaction in the Mathematical Statistics II course. In addition, the service quality also affects the understanding level significantly and positively. However, the level of student understanding has no significant effect on the level of student satisfaction. The coefficient of determination (R2) of the structural model of student satisfaction with online learning for the Mathematical Statistics II course is 0.774. This value indicates that the resulting model is good because it has relatively high accuracy.
Design of High gain Boost Converter for Standalone applications

The main problem is renewable energy sources like solar pv arrays is they are operating at very low voltage which is insufficient for many standalone applications where voltage gain requirement high. For these type of applications high efficiency, high voltage gain and reduction in switch voltage, component count, ripple in input current are the priority features. To handle this issue a high voltage gain boost converter is presented. The converter with the combination of coupled inductors and voltage-doubler structure is proposed to achieve high voltage gain. Voltage-doubler structure is kept in the secondary winding of the coupled inductor which decrease the diode voltage stress. This converter output is fed as input to a neutral point clamped inverter and is connected to the 3induction motor. There is a implementation of maximum power point tracking (MPPT) perturbance observance algorithm for a photo voltaic system so as to extract maximum power from the solar cell, also to calculate the reference speed to the induction motor due to change in irradiance and temperature of PV cell.
A DATA ENVELOPMENT ANALYSIS BASED FOR EVALUATING EFFICIENCY OF BUS PUBLIC ROUTES

The public transport service is highly essential to meet the demand due to a rapidly growing population and mobility. Thus providing public service and improve its service becomes an urgent need in recent years. In Iraq, the Bus system represents the backbone in public transportation, which is based mainly on highway infrastructure. To meet the growing mobility needs, enhancing public service provided only by bus routes is essential. Measuring bus route performance represents one of the crucial transit research topics in the last recent years. The current study tries to investigate the urban public route's efficiency utilizing the "data envelopment analysis (DEA)" technique. To analyze route performance, DEA is using, and performance measures include route design, cost, service, operation, and comfort efficiency are selected and calculated for different routes. Efficiency and effectiveness are the output of this process. Bus company owners can also use the results of this study to improve their services, attract new customers, and better manage their resources.
Field correlation between shear wave velocity measured by Panda 3®, cone penetration test (CPT) and geophysical tests

This paper will present a field comparative study between shear wave velocity measurements (Vs), deduced from compression wave velocity (Vp) and signal analysis with Panda 3® test, and those obtained by correlation through CPT tests and seismic surface wave method (MASW) on a sandy site in southern of France. The purpose of this paper is to confirm the consistency of Panda 3® shear wave measurements with measures resulting from traditional geotechnical investigation methods.
SYNTHESIS OF CARBON NANOTUBES BY THE ELECTRIC ARC-DISCHARGE METHOD

The paper presents the synthesis of carbon nanotubes (CNTs) by the method of electric arc-discharge. To obtain CNTs, the process of an electric arc-discharge of graphite was carried out; the current strength at a constant voltage of 50 V was 120, 150, 170, 200 A (inert medium argon). Using the methods of electron microscopy, BET and Raman scattering, the chemical composition, specific surface area were determined, and the surface morphology of the obtained samples was studied. The results of determining the specific surface area and pore volume show that with increasing current strength, the specific surface area and specific pore volume increase from 25.450 to 159.737 m2/g and from 0.011 cm3/g to 0.068 cm3/ g, respectively. Electron microscopic images show CNT particles with a diameter of 58 to 370 nm, which are rolled into a single, seamless, cylindrical shape.
Health Monitoring of Civil Structures: A MCMC Approach Based on a Multi-Fidelity Deep Neural Network Surrogate

To meet the need for reliable real-time monitoring of civil structures, safety control and optimization of maintenance operations, this paper presents a computational method for the stochastic estimation of the degradation of the load bearing structural properties. Exploiting a Bayesian framework, the procedure sequentially updates the posterior probability of the damage parameters used to describe the aforementioned degradation, conditioned on noisy sensors observations, by means of Markov chain Monte Carlo (MCMC) sampling algorithms. To enable the analysis to run in real-time or quasi real-time, the numerical model of the structure is replaced with a data-driven surrogate used to evaluate the (conditional) likelihood function. The proposed surrogate model relies on a multi-fidelity (MF) deep neural network (DNN), mapping the damage and operational parameters onto sensor recordings. The MF-DNN is shown to effectively leverage information between multiple datasets, by learning the correlations across models with different fidelities without any prior assumption, ultimately alleviating the computational burden of the supervised training stage. The low fidelity (LF) responses are approximated by relying on proper orthogonal decomposition for the sake of dimensionality reduction, and a fully connected DNN. The high fidelity signals, that feed the MCMC within the outer-loop optimization, are instead generated by enriching the LF approximations through a deep long short-term memory network. Results relevant to a specific case study demonstrate the capability of the proposed procedure to estimate the distribution of damage parameters, and prove the effectiveness of the MF scheme in outperforming a single-fidelity based method.
LOGAN: Unpaired Shape Transform in Latent Overcomplete Space

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Effects of compression garment on muscular efficacy, proprioception, and recovery after exercise-induced muscle fatigue onset for people who exercise regularly

Fatigue is a major cause of exercise-induced muscle damage (EIMD). Compression garments (CGs) can aid post-exercise recovery, therefore, this study explored the effects of CGs on muscular efficacy, proprioception, and recovery after exercise-induced muscle fatigue in people who exercise regularly. Twelve healthy participants who exercised regularly were enrolled in this study. Each participant completed an exercise-induced muscle fatigue test while wearing a randomly assigned lower-body CG or sports pants (SP); after at least 7 days, the participant repeated the test while wearing the other garment. The dependent variables were muscle efficacy, proprioception (displacements of center of pressure/COP, and absolute error), and fatigue recovery (muscle oxygen saturation/SmO2, deoxygenation and reoxygenation rate, and subjective muscle soreness). A two-way repeated measure analysis of variance was conducted to determine the effect of garment type. The results indicated that relative to SP use, CG use can promote muscle efficacy, proprioception in ML displacement of COP, and fatigue recovery. Higher deoxygenation and reoxygenation rates were observed with CG use than with SP use. For CG use, SmO2 quickly returned to baseline value after 10 min of rest and was maintained at a high level until after 1 h of rest, whereas for SP use, SmO2 increased with time after fatigue onset. ML displacement of COP quickly returned to baseline value after 10 min of rest and subsequently decreased until after 1 hour of rest. Relative to SP use, CG use was associated with a significantly lower ML displacement after 20 min of rest. In conclusion, proprioception and SmO2 recovery was achieved after 10 min of rest; however, at least 24 h may be required for recovery pertaining to muscle efficacy and soreness regardless of CG or SP use.