Momentum Rate Research Articles

Collapse of turbulent massive cores with ambipolar diffusion and hybrid radiative transfer

Context. Most massive protostars exhibit bipolar outflows. Nonetheless, there is no consensus regarding the mechanism at the origin of these outflows, nor on the cause of the less-frequently observed monopolar outflows. Aims. We aim to identify the origin of early massive protostellar outflows, focusing on the combined effects of radiative transfer and magnetic fields in a turbulent medium. Methods. We use four state-of-the-art radiation-magnetohydrodynamical simulations following the collapse of massive 100 M⊙ pre-stellar cores with the RAMSES code. Turbulence is taken into account via initial velocity dispersion. We use a hybrid radiative transfer method and include ambipolar diffusion. Results. Turbulence delays the launching of outflows, which appear to be mainly driven by magnetohydrodynamical processes. We study both the magnetic tower flow and the magneto-centrifugal acceleration as possible origins. Both contribute to the acceleration and the former operates on larger volumes than the latter. Our finest resolution, 5 AU, does not allow us to get converged results on magneto-centrifugally accelerated outflows. Radiative acceleration takes place as well, dominates in the star vicinity, enlarges the outflow extent, and has no negative impact on the launching of magnetic outflows (up to M ~17 M⊙, L ~ 105 L⊙). We observe mass outflow rates of 10−5−10−4 M⊙ yr−1 and momentum rates of the order ~10−4 M⊙ km s−1 yr−1. The associated opening angles (20−30deg when magnetic fields dominate) are in a range between observed values for wide-angle outflows and collimated outflows. If confirmed with a finer numerical resolution at the outflow interface, this suggests additional (de-)collimating effects. Outflows are launched nearly perpendicular to the disk and are misaligned with the initial core-scale magnetic fields, in agreement with several observational studies. In the most turbulent run, the outflow is monopolar. Conclusions. Magnetic processes are dominant over radiative ones in the acceleration of massive protostellar outflows of up to ~17 M⊙. Turbulence perturbs the outflow launching and is a possible explanation for monopolar outflows.

Hybrid generative regression-based deep intelligence to predict the risk of chronic disease

PurposeChronic diseases are considered as one of the serious concerns and threats to public health across the globe. Diseases such as chronic diabetes mellitus (CDM), cardio vasculardisease (CVD) and chronic kidney disease (CKD) are major chronic diseases responsible for millions of death. Each of these diseases is considered as a risk factor for the other two diseases. Therefore, noteworthy attention is being paid to reduce the risk of these diseases. A gigantic amount of medical data is generated in digital form from smart healthcare appliances in the current era. Although numerous machine learning (ML) algorithms are proposed for the early prediction of chronic diseases, these algorithmic models are neither generalized nor adaptive when the model is imposed on new disease datasets. Hence, these algorithms have to process a huge amount of disease data iteratively until the model converges. This limitation may make it difficult for ML models to fit and produce imprecise results. A single algorithm may not yield accurate results. Nonetheless, an ensemble of classifiers built from multiple models, that works based on a voting principle has been successfully applied to solve many classification tasks. The purpose of this paper is to make early prediction of chronic diseases using hybrid generative regression based deep intelligence network (HGRDIN) model.Design/methodology/approachIn the proposed paper generative regression (GR) model is used in combination with deep neural network (DNN) for the early prediction of chronic disease. The GR model will obtain prior knowledge about the labelled data by analyzing the correlation between features and class labels. Hence, the weight assignment process of DNN is influenced by the relationship between attributes rather than random assignment. The knowledge obtained through these processes is passed as input to the DNN network for further prediction. Since the inference about the input data instances is drawn at the DNN through the GR model, the model is named as hybrid generative regression-based deep intelligence network (HGRDIN).FindingsThe credibility of the implemented approach is rigorously validated using various parameters such as accuracy, precision, recall, F score and area under the curve (AUC) score. During the training phase, the proposed algorithm is constantly regularized using the elastic net regularization technique and also hyper-tuned using the various parameters such as momentum and learning rate to minimize the misprediction rate. The experimental results illustrate that the proposed approach predicted the chronic disease with a minimal error by avoiding the possible overfitting and local minima problems. The result obtained with the proposed approach is also compared with the various traditional approaches.Research limitations/implicationsUsually, the diagnostic data are multi-dimension in nature where the performance of the ML algorithm will degrade due to the data overfitting, curse of dimensionality issues. The result obtained through the experiment has achieved an average accuracy of 95%. Hence, analysis can be made further to improve predictive accuracy by overcoming the curse of dimensionality issues.Practical implicationsThe proposed ML model can mimic the behavior of the doctor's brain. These algorithms have the capability to replace clinical tasks. The accurate result obtained through the innovative algorithms can free the physician from the mundane care and practices so that the physician can focus more on the complex issues.Social implicationsUtilizing the proposed predictive model at the decision-making level for the early prediction of the disease is considered as a promising change towards the healthcare sector. The global burden of chronic disease can be reduced at an exceptional level through these approaches.Originality/valueIn the proposed HGRDIN model, the concept of transfer learning approach is used where the knowledge acquired through the GR process is applied on DNN that identified the possible relationship between the dependent and independent feature variables by mapping the chronic data instances to its corresponding target class before it is being passed as input to the DNN network. Hence, the result of the experiments illustrated that the proposed approach obtained superior performance in terms of various validation parameters than the existing conventional techniques.

CFD-PBM modelling of tailings flocculation in a lab-scale gravity thickener

Flocculation occurring in a feedwell plays a critical role in tailings slurry thickening process, which is complicated and significantly influenced by flow characteristics. This work presents a numerical approach to explore the effect of flow characteristics on flocculation performance. It combines an aggregation kernel and a breakage kernel, used to describe the polymer-bridging flocculation kinetics, with a conventional Computational Fluid Dynamics-Population Balance Model (CFD-PBM) coupling to model the complex flocculation-thickening behavior in a lab-scale gravity thickener. The solid-liquid phase interaction is described by an Euler-Euler approach with a modified Schiller-Naumann drag model. The turbulence of liquid phase is resolved by the RNG k-ε turbulence model, while the solid kinematic eddy viscosity is described by a dispersed phase zero equation model. The capability of this proposed model is validated by a good agreement between experimental and predicted results in terms of single-liquid velocity and floc size distribution. The momentum and turbulence dissipation rates are investigated in and around the feedwell over a wide range of feed velocities, showing that the momentum and turbulence dissipation rates have a positive correlation with feed velocity. The momentum and turbulence dissipation rates decrease with the increase in scaled depth in the feedwell. The formation of large vortexes in the feedwell may cause a locally low turbulence dissipation rate. A reasonable increase of feed velocity favours the flocculation, however, an excessive feed velocity can cause a decrease in mean floc size. Modelling tailings flocculation is of great significance for understanding the flocculation behavior and revealing the effect of flow characteristics on flocculation performance.

Discovery of a Highly Collimated Flow from the High-mass Protostar ISOSS J23053+5953 SMM2

Abstract We present Very Large Array C-, X-, and Q-band continuum observations, as well as 1.3 mm continuum and CO(2-1) observations with the Submillimeter Array toward the high-mass protostellar candidate ISOSS J23053+5953 SMM2. Compact centimeter continuum emission was detected near the center of the SMM2 core with a spectral index of 0.24(± 0.15) between 6 and 3.6 cm, and a radio luminosity of 1.3(±0.4) mJy kpc2. The 1.3 mm thermal dust emission indicates a mass of the SMM2 core of 45.8 (±13.4) M ⊙, and a density of 7.1 (±1.2)× 106 cm−3. The CO(2-1) observations reveal a large, massive molecular outflow centered on the SMM2 core. This fast outflow (>50 km s−1 from the cloud systemic velocity) is highly collimated, with a broader, lower-velocity component. The large values for outflow mass (45.2 ± 12.6 M ⊙) and momentum rate (6 ± 2 × 10−3 M ⊙ km s−1yr−1) derived from the CO emission are consistent with those of flows driven by high-mass YSOs. The dynamical timescale of the flow is between 1.5 and 7.2 × 104 yr. We also found from the C18O to thermal dust emission ratio that CO is depleted by a factor of about 20, possibly due to freeze-out of CO molecules on dust grains. Our data are consistent with previous findings that ISOSS J23053 + 5953 SMM2 is an emerging high-mass protostar in an early phase of evolution, with an ionized jet and a fast, highly collimated, and massive outflow.

Turbulent cylinder-stirred flow heat and momentum transfer research in batch operated single-phase square reactor

Heat transfer and momentum transfer due to water flow in a cylinder-stirred reactor of square cross section were investigated. Important industrial processes like mixing depend on combined effects of heat transfer and fluid flow which can be understood by analyzing the turbulence structure. A reactor of aspect ratio η = 0.21 (diameter of cylinder to tank side wall) was considered. It uses a small-diameter cylinder to stir the water in batch condition with rotating Reynolds number in a range 1.68 × 104 <Re < 10.1 × 104. The cylinder released a constant heat flux rate of 158 W/m2 through its surface. The steady state mean flow was numerically simulated using two models for turbulence, isothermally with a k-ε and thermally with the RSM model. The results revealed a fluid motion like Taylor-Couette vortex which was validated with PIV streamlines for Re = 6.1 × 104 and 10.1 × 104. Turbulent angular momentum and shear rate revealed differences for the corner direction as the Re number increased compared with the wall direction. Temperature fluctuations and thermal gradient in the gap allowed to analyze the turbulent heat diffusivity coefficient and the ratio to predicted diffusivity for momentum. The results revealed that heat and momentum diffusivity increase for higher Re number and show that heat diffusivity is faster than momentum in the gap. The main frequencies of fluid motion showed large-scale structures and secondary cells of fluid motion. The averaged heat transfer as a function of Re number indicate that this reactor is an enhanced mean for mixing process compared to concentric cylinders reactors.

Characterizing mass, momentum, energy, and metal outflow rates of multiphase galactic winds in the FIRE-2 cosmological simulations

ABSTRACT We characterize mass, momentum, energy, and metal outflow rates of multiphase galactic winds in a suite of FIRE-2 cosmological ‘zoom-in’ simulations from the Feedback in Realistic Environments (FIRE) project. We analyse simulations of low-mass dwarfs, intermediate-mass dwarfs, Milky Way-mass haloes, and high-redshift massive haloes. Consistent with previous work, we find that dwarfs eject about 100 times more gas from their interstellar medium (ISM) than they form in stars, while this mass ‘loading factor’ drops below one in massive galaxies. Most of the mass is carried by the hot phase (&amp;gt;105 K) in massive haloes and the warm phase (103−105 K) in dwarfs; cold outflows (&amp;lt;103 K) are negligible except in high-redshift dwarfs. Energy, momentum, and metal loading factors from the ISM are of order unity in dwarfs and significantly lower in more massive haloes. Hot outflows have 2−5 × higher specific energy than needed to escape from the gravitational potential of dwarf haloes; indeed, in dwarfs, the mass, momentum, and metal outflow rates increase with radius whereas energy is roughly conserved, indicating swept up halo gas. Burst-averaged mass loading factors tend to be larger during more powerful star formation episodes and when the inner halo is not virialized, but we see effectively no trend with the dense ISM gas fraction. We discuss how our results can guide future controlled numerical experiments that aim to elucidate the key parameters governing galactic winds and the resulting associated preventative feedback.

A Spatially Resolved Survey of Distant Quasar Host Galaxies. I. Dynamics of Galactic Outflows

Abstract We present observations of ionized gas outflows in 11 z = 1.39–2.59 radio-loud quasar host galaxies. Data were taken with the integral field spectrograph OSIRIS and the adaptive optics system at the W.M. Keck Observatory targeting nebular emission lines (Hβ, [O III], Hα, [N II], and [S II]) redshifted into the near-infrared (1–2.4 μm). Outflows with velocities of 500–1700 km s−1 are detected in 10 systems on scales ranging from &lt;1 kpc to 10 kpc with outflow rates from 8–2400 M ⊙ yr−1 . For five sources, the outflow momentum rates are 4–80 times L AGN/c, consistent with outflows being driven by an energy-conserving shock. The five other outflows are either driven by radiation pressure or an isothermal shock. The outflows are the dominant source of gas depletion, and we find no evidence for star formation along the outflow paths. For eight objects, the outflow paths are consistent with the orientation of the jets. Yet, given the calculated pressures, we find no evidence of the jets currently doing work on these galactic-scale ionized outflows. We find that galactic-scale feedback occurs well before galaxies establish a substantial fraction of their stellar mass, as expected from local scaling relationships.

Enhancement of multilayer perceptron model training accuracy through the optimization of hyperparameters: a case study of the quality prediction of injection-molded parts

Injection molding has been broadly used in the mass production of plastic parts and must meet the requirements of efficiency and quality consistency. Machine learning can effectively predict the quality of injection-molded part. However, the performance of machine learning models largely depends on the accuracy of the training. Hyperparameters such as activation functions, momentum, and learning rate are crucial to the accuracy and efficiency of model training. This research aims to analyze the influence of hyperparameters on testing accuracy, explore the corresponding optimal learning rate, and provide the optimal training model for predicting the quality of injection-molded parts. In this study, stochastic gradient descent (SGD) and stochastic gradient descent with momentum (SGDM) are used to optimize the artificial neural network model. Through optimization of these training model hyperparameters, the width testing accuracy of the injection product is improved. The experimental results indicate that in the absence of momentum effects, all five activation functions can achieve more than 90% of the training accuracy with a learning rate of 0.1. Moreover, when optimized with the SGD, the learning rate of the Sigmoid activation function is 0.1, and the testing accuracy reaches 95.8%. Although momentum has the least influence on accuracy, it affects the convergence speed of the Sigmoid function, which reduces the number of required learning iterations (82.4% reduction rate). In summary, optimizing hyperparameter settings can improve the accuracy of model testing and markedly reduce training time.

Two-wind interactions in binaries with two orbiting giant stars

ABSTRACT Some red giant envelopes present spiral structures (seen either in dust-scattered stellar continuum or in molecular line emission), the most striking example probably being AFGL 3068. This object has been modeled (both analytically and numerically) in terms of a wind ejected from a star in orbit around a binary companion. We revisit both analytical models and 3D simulations of a wind from an orbiting red giant star, and extend the numerical simulations to the case of a binary with two red giants with strong winds. We find that most two-wind models on the orbital plane show a ‘double spiral’ structure close to the binary source, and that these two arms merge into a single spiral structure at larger distances. However, for the case of a binary with two identical winds, the two spiral arms are still present at large distances from the binary source. We also find that for models of two (not identical) dynamically important winds, a region close to the orbital plane has material from both winds. Also, an approximately conical region centered on the orbital axis is filled exclusively by the wind with larger momentum rate. These two structures lead to morphologies reminiscent of the so-called ‘hour glass’ planetary nebulae. Finally, we find that increasing wind velocity disparities lead to the formation of clumpy structures along the spiral arms. Observations of ‘clumpy spirals’ are therefore likely to indicate the presence of two strong winds from the stars in the central binary system.

Prediction of Groundwater Level in The Shallow Aquifer Using Artificial Neural Network Approach

In these regions, groundwater is often the most significant source of water. Groundwater level estimation accuracy is a vital component of efficient water supply management. In this paper, an artificial neural network (ANN) with gradient descent with momentum and adaptive learning rate backpropagation algorithm for groundwater level forecasting applications is proposed. The ANN model used an 8-5-3-1 and 8-10-5-1 network architecture with the input parameter of the form such as precipitation, evaporation, atmospheric pressure, wind, humidity, long exposure to the sun and temperature simultaneously and a relatively short length of groundwater level data recorded from January 2017 to December 2019 at two observation wells in the North Denpasar, Bali, Indonesia. The study’s findings show that ANN models can predict groundwater levels. It is suggested that more research be conducted on this proposed process, which can then be used to help establish and incorporate more efficient and long-term groundwater management strategies.

Examining Asset Pricing Anomalies: Evidence from Europe

We examine five important asset pricing anomalies, namely, size, value, momentum, profitability, and investment rate to evaluate their efficacy in major West European economies, that is, France, Germany, Italy, and Spain. We employ four prominent asset pricing models, namely Capital Asset Pricing Model (CAPM), Fama–French three-factor (FF3) model, Carhart model and Fama–French five-factor (FF5) model to evaluate whether portfolio managers can create trading strategies to generate risk-adjusted extra normal returns for their investors. We also examine the prominent anomalies which pass the test of asset pricing in our sample countries and evaluate the best performing asset pricing model in explaining returns in each of these countries. We find that in spite of being matured markets, these countries provide portfolio managers with opportunities to exploit these strategies to generate extra normal returns for their investors. Momentum anomaly for Germany and profitability anomaly for Italy can be exploited by fund managers for generating risk-adjusted returns. For France, except for net investment rate anomaly, all the other anomalies remained unexplained by asset pricing models. We also find CAPM to be the better model in explaining returns of Italy and Spain. While FF3 factor and FF5 factor models explain returns in Germany, our sample asset pricing models failed to work for France. Our study has implications for portfolio managers, academia, and policymakers.

Analysis and Classification of Motor Imagery Using Deep Neural Network

Motor imagery based on brain-computer interface (BCI) has attracted important research attention despite its difficulty. It plays a vital role in human cognition and helps in making the decision. Many researchers use electroencephalogram (EEG) signals to study brain activity with left and right-hand movement. Deep learning (DL) has been employed for motor imagery (MI). In this article, a deep neural network (DNN) is proposed for classification of left and right movement of EEG signal using Common Spatial Pattern (CSP) as feature extraction with standard gradient descent (GD) with momentum and adaptive learning rate LR. (GDMLR), the performance is compared using a confusion matrix, the average classification accuracy is 87%, which is improved as compared with state-of-the-art methods that used different datasets.

2D electrons floating on a suspended atomically thin dielectric

The 2D electrons trapped in vacuum near the atomically thin dielectric (ATD, mono- or N-layer film of h-BN or transition metal dichalcogenide) are considered. ATD is suspended above the back gate and forms the capacitor, which is controlled by the biased voltage determining 2D concentration, n2D. It is found that the leakage current through ATD is negligible, and the effect of the polarizability of ATD is weak if N≤5. At temperatures T=0.1–15 K and n2D=5×108–1010cm−2, one deals with the Boltzmann liquid having a macroscopic thickness of ∼100 A. Due to the bending of ATD, the quadratic dispersion law of the flexural vibrations is transformed into the linear one at small wave vectors. The scattering processes of the electrons caused by these phonons or the monolayer islands on ATD are examined and the momentum and energy relaxation rates are analyzed based on the corresponding balance equations. The momentum relaxation times vary over orders of magnitude in the above region (T, n2D) and N. The response may change from the polaron transport, for a perfect single-layer ATD at low T and high n2D, to the high-mobility (≥107cm2/Vs) regime at high T and low n2D. The quasi-elastic energy relaxation due to phonon-induced scattering is considered, and the conditions for the heating of electrons by a weak in-plane electric field are found.

The characteristic momentum of radiatively cooling energy-driven galactic winds

ABSTRACT Energy injection by supernovae may drive hot supersonic galactic winds in rapidly star-forming galaxies, driving metal-enriched gas into the circumgalactic medium and potentially accelerating cool gas. If sufficiently mass-loaded, such flows become radiative within the wind-driving region, reducing the overall mass outflow rate from the host galaxy. We show that this sets a maximum on the total outflow momentum for hot energy-driven winds. For a spherical wind of Solar metallicity driven by continuous star formation, $\dot{p}_\mathrm{max} \simeq 1.9\times 10^4\ M_\odot \ \mathrm{yr}^{-1}\ \mathrm{km\ s}^{-1}(\alpha /0.9)^{0.86}\left[R_\star /(300\ \mathrm{pc})\right]^{0.14}[\dot{M}_\star /(20\ M_\odot \ \mathrm{yr}^{-1})]^{0.86},$ where α is the fraction of supernova energy that thermalizes the wind, and $\dot{M}_\star$ and R⋆ are the star formation rate and radius of the wind-driving region. This maximum momentum for hot winds can also apply to cool, ionized outflows that are typically observed in starburst galaxies, if the hot wind undergoes bulk radiative cooling or if the hot wind transfers mass and momentum to cool clouds within the flow. We show that requiring the hot wind to undergo single-phase cooling on large scales sets a minimum on the total outflow momentum rate. These maximum and minimum outflow momenta have similar values, setting a characteristic momentum rate of hot galactic winds that can become radiative on large scales. We find that most observations of photoionized outflow wind momentum fall below the theoretical maximum and thus may be signatures of cooling hot flows. On the other hand, many systems fall below the minimum momentum required for bulk cooling, indicating that perhaps the cool material observed has instead been entrained in or mixed with the hot flow.

Pressure Drop, Air Entrainment, and Moments of the Axial Velocity in the Laminar-Turbulent Transition Jet Flow in Domestic Gas Injectors

Abstract This work aims at characterizing the flow in the outlet of three gas injectors used in atmospheric burners and developing correlations for the discharge coefficient, air entrainment, momentum, and energy flow rates. These devices have millimeter-sized orifices, a cup-like region at the injector outlet, and the flow occurs in the transition from the laminar to the fully turbulent regimes. The pressure drop was measured and correlated as a function of the orifice Reynolds number for the three injectors. The correlations are able to predict the discharge coefficient within ±5% deviation from the measurements in the range 90≤Re≤4400. The axial velocity and turbulent intensity were measured at the outlet of the injectors using a hot-wire anemometer at the orifice Reynolds number of 3060, which is typical of the applications. The measurements were compared to computational fluid dynamics (CFD) solutions using the γ−Reθ Reynolds-averaged Navier–Stokes transition model in the star-ccm+ commercial package. The results indicate the strong influence of the shape of the outlet cup-like region of the injectors on the development of an internal mixing layer and the external mixing layer in the free jet. The momentum and energy flow rates for the injector model with the largest cup are reduced to 50% and 21%, respectively, of the simplest gas injector. However, the gas jet in this injector carries 28% of the stoichiometric air before leaving the cup. These aspects must be taken into account in the preliminary design of atmospheric burners.

The CALYPSO IRAM-PdBI survey of jets from Class 0 protostars

Aims. As a part of the CALYPSO Large Program, we aim to constrain the properties of protostellar jets and outflows by searching for corresponding emission in a sample of 21 Class 0 protostars covering a broad range of internal luminosities (Lint from 0.035 L⊙ to 47 L⊙). Methods. We analyzed high angular (~0.′′5–1.′′0) resolution IRAM Plateau de Bure Interferometer (PdBI) observations in three typical outflow and jet tracers, namely: CO (2−1), SO (56−45), and SiO (5−4). We obtained the detection rate, spatial distribution, kinematics, and collimation of the outflow and jet emission in the three lines. Molecular column densities and abundances, the jet mass-loss and momentum rates, and mechanical luminosity are estimated from the integrated line intensities. Results. Blue- and red-shifted emission in CO (2−1), which probes outflowing gas, was detected in all the sources in the sample and detected for the first time in the cases of SerpS-MM22 and SerpS-MM18b. Collimated high-velocity jets in SiO (5−4) were detected in 67% of the sources (for the first time in IRAS4B2, IRAS4B1, L1448-NB, SerpS-MM18a) and 77% of these also showed jet or outflow emission in SO (56−45). The detection rate of jets in SiO and SO increases with internal luminosity. In five sources (that is, 24% of the sample) SO (56−45) is elongated and reveals a velocity gradient perpendicular to the jet direction, hence, it probes the inner envelope or the disk, or both. The detected SiO jets are collimated (typical opening angle, α ~ 10°) and surrounded by wider angle SO (α ~ 15°) and CO (α ~ 25°) emission. The abundance of SO relative to H2 ranges from &lt;10−7 up to 10−6; whereas for SiO, the lower limits were found to range from &gt;2.4 × 10−7 to &gt;5 × 10−6, with the exception of the jets from IRAS4A1 and IRAS4A2, which show low SiO abundance (≤2−6 × 10−8). The mass-loss rates range from ~7 × 10−8 M⊙ yr−1 up to ~3 × 10−6 M⊙ yr−1 for Lint ~ 1−50 L⊙. Conclusions. The CALYPSO IRAM-PdBI survey shows that the outflow phenomenon is ubiquitous in our sample of protostars and that the detection rate of high-velocity collimated jets increases for increasing protostellar accretion, with at least 80% of the sources with Lint &gt; 1 L⊙ driving a jet. The detected protostellar flows exhibit an onion-like structure, where the SiO jet is nested into a wider angle SO and CO outflow. On scales &gt; 300 au the SiO jets are less collimated (4°−12°) than atomic jets from Class II sources (~3°), possibly dueto projection effects and contamination by SiO emission from the bow-shocks. On the other hand, velocity asymmetry between the two jet lobes are detected in one third of the sources, similarly to Class II atomic jets, suggesting that the same launching mechanism is at work. Most of the jets are SiO rich, which indicates very efficient release of &gt; 1− 10% of elemental silicon in gas phase likely in dust-free winds, launched from inside the dust sublimation radius. The estimated mass-loss rates are larger by up to five orders of magnitude than what was measured for Class II jets, indicating that the ejection decreases as the source evolves and accretion fades. Similarly to Class II sources, the mass-loss rates are ~ 1− 50% of the mass accretion rate, Ṁacc, suggesting that the correlation between mass ejection and mass accretion holds along the star-formation process from 104 yr up to a few Myr.

VGG16 Transfer Learning Architecture for Salak Fruit Quality Classification

Purpose: This study aims to differentiate the quality of salak fruit with machine learning. Salak is classified into two classes, good and bad class.Design/methodology/approach: The algorithm used in this research is transfer learning with the VGG16 architecture. Data set used in this research consist of 370 images of salak, 190 from good class and 180 from bad class. The image is preprocessed by resizing and normalizing pixel value in the image. Preprocessed images is split into 80% training data and 20% testing data. Training data is trained by using pretrained VGG16 model. The parameters that are changed during the training are epoch, momentum, and learning rate. The resulting model is then used for testing. The accuracy, precision and recall is monitored to determine the best model to classify the images.Findings/result: The highest accuracy obtained from this study is 95.83%. This accuracy is obtained by using a learning rate = 0.0001 and momentum 0.9. The precision and recall for this model is 97.2 and 94.6.Originality/value/state of the art: The use of transfer learning to classify salak which never been used before.

Laser fusion cutting: evaluation of gas boundary layer flow state, momentum and heat transfer

The present work deals with the evaluation of gas boundary layer characteristics under conditions of a high-pressurized gas flow through narrow kerfs as prevalent in laser fusion cutting. A simplistic two-dimensional channel model with appropriate boundary conditions in combination with empirical correlations of the similitude theory is applied to determine the flow state and the thickness of the boundary layer as well as magnitudes of momentum and heat transfer rates. The estimations show that the most expectable flow state of the boundary layer corresponds to a transitional regime. Calculated boundary layer thicknesses lie in a range of 100 to 300 microns after a considered running length of 10 mm. Thus, the formation of the characteristic cut edge topography with typical maximum roughness values for Rz of about 50 microns for high-quality solid-state laser fusion cuts will take place within the boundary layer region. It can be concluded, that the knowledge of the particular spatial and temporal flow structure of the boundary layer should be considered of being indispensable for a profound understanding of the formation mechanisms of the cut edge topography.

Multi-scale view of star formation in IRAS 21078+5211: from clump fragmentation to disk wind

Context. Star formation (SF) is a multi-scale process in which the mode of fragmentation of the collapsing clump on scales of 0.1–1 pc determines the mass reservoir and affects the accretion process of the individual protostars on scales of 10–100 au. Aims. We want to investigate the nearby (located at 1.63 ± 0.05 kpc) high-mass star-forming region IRAS 21078+5211 at linear scales from ~1 pc down to ~10 au. Methods. We combine the data of two recent programs: the NOrthern Extended Millimeter Array large project CORE and the Protostellar Outflows at the EarliesT Stages (POETS) survey. The former provides images of the 1 mm dust continuum and molecular line emissions with a linear resolution of ≈600 au covering a field of view up to ≈0.5 pc. The latter targets the ionized gas and 22 GHz water masers, mapping linear scales from a few 103 au down to a few astronomical units. Results. In IRAS 21078+5211, a highly fragmented cluster (size ~0.1 pc) of molecular cores is observed, located at the density peak of an elongated (size ~1 pc) molecular cloud. A small (≈1 km s−1 per 0.1 pc) LSR velocity (VLSR) gradient is detected across the major axis of the molecular cloud. Assuming we are observing a mass flow from the harboring cloud to the cluster, we derive a mass infall rate of ≈10−4 M⊙ yr−1. The most massive cores (labeled 1, 2, and 3) are found at the center of the cluster, and these are the only ones that present a signature of protostellar activity in terms of emission from high-excitation molecular lines or a molecular outflow. The masses of the young stellar objects (YSOs) inside these three cores are estimated in the range 1–6 M⊙. We reveal an extended (size ~0.1 pc), bipolar collimated molecular outflow emerging from core 1. We believe this is powered by the compact (size ≲1000 au) radio jet discovered in the POETS survey, ejected by a YSO embedded in core 1 (named YSO-1), since the molecular outflow and the radio jet are almost parallel and have a comparable momentum rate. By means of high-excitation lines, we find a large (≈14 km s−1 over 500 au) VLSR gradient at the position of YSO-1, oriented approximately perpendicular to the radio jet. Assuming this is an edge-on, rotating disk and fitting a Keplerian rotation pattern, we determine the YSO-1 mass to be 5.6 ± 2.0 M⊙. The water masers observed in the POETS survey emerge within 100–300 au from YSO-1 and are unique tracers of the jet kinematics. Their three-dimensional (3D) velocity pattern reveals that the gas flows along, and rotates about, the jet axis. We show that the 3D maser velocities are fully consistent with the magneto-centrifugal disk-wind models predicting a cylindrical rotating jet. Under this hypothesis, we determine the jet radius to be ≈ 16 au and the corresponding launching radius and terminal velocity to be ≈ 2.2 au and ≈ 200 km s−1, respectively. Conclusions. Complementing high-angular resolution, centimeter and millimeter interferometric observations in thermal tracers with Very Long Baseline Interferometry of molecular masers, is invaluable in studying high-mass SF. The combination of these twodatasets allows us to connect the events that we see at large scales, as clump fragmentation and mass flows, with the physical processes identified at small scales, specifically, accretion and ejection in disk-jet systems.

Shape optimization of a three-dimensional membrane-structured solar sail using an angular momentum unloading strategy

A shape of the satellite’s solar sail membrane is essential for unloading angular momentum in the three-axis stabilized attitude control system because the three-dimensional solar sail can receive solar radiation pressure from arbitrary directions. In this paper, the objective is the shape optimization of a three-dimensional membrane-structured solar sail using the angular momentum unloading strategy. We modelled and simulated the solar radiation pressure torque, for unloading angular momentum. Using the simulation system, since the unloading angular momentum rate is maximized, the shape of the three-dimensional solar sail was optimized using a Genetic algorithm and Sequential Quadratic Programming. The unloading velocity in the optimized shaped solar sail was greatly improved with respect to a conventional flat or pyramid solar sail.