Miracle-dz/UniFAR-FTUs huggingface dataset:
A dearth of small particles in debris disks - An energy-constrained smallest fragment size

A prescription for the fragment size distribution resulting from dust grain collisions is essential when modelling a range of astrophysical systems, such as debris disks and planetary rings. While the slope of the fragment size distribution and the size of the largest fragment are well known, the behaviour of the distribution at the small size end is theoretically and experimentally poorly understood. This leads debris disk codes to generally assume a limit equal to, or below, the radiation blow-out size. We use energy conservation to analytically derive a lower boundary of the fragment size distribution for a range of collider mass ratios. Focussing on collisions between equal-sized bodies, we apply the method to debris disks. For a given collider mass, the size of the smallest fragments is found to depend on collision velocity, material parameters, and the size of the largest fragment. We provide a physically motivated recipe for the calculation of the smallest fragment, which can be easily implemented in codes for modelling collisional systems. For plausible parameters, our results are consistent with the observed predominance of grains much larger than the blow-out size in Fomalhaut's main belt and in the Herschel cold debris disks.
Evolution of CO lines in time-dependent models of protostellar disk formation

Context. Star and planet formation theories predict an evolution in the density, temperature, and velocity structure as the envelope collapses and forms an accretion disk. While continuum emission can trace the dust evolution, spectrally resolved molecular lines are needed to determine the physical structure and collapse dynamics. Aims. The aim of this work is to model the evolution of the molecular excitation, line profiles, and related observables during lowmass star formation. Specifically, the signatures of disks during the deeply embedded stage (Menv > M� ) are investigated. Methods. The semi-analytic 2D axisymmetric model of Visser and collaborators has been used to describe the evolution of the density, stellar mass, and luminosity from the pre-stellar to the T-Tauri phase. A full radiative transfer calculation is carried out to accurately determine the time-dependent dust temperatures. The time-dependent CO abundance is obtained from the adsorption and thermal desorption chemistry. Non-LTE near-IR (NIR), far-IR (FIR), and submm lines of CO have been simulated at a number of time steps. Results. In single dish (10−20 �� beams), the dynamics during the collapse are best probed through highly excited 13 CO and C 18 O lines, which are significantly broadened by the infall process. In contrast to the dust temperature, the CO excitation temperature derived from submm/FIR data does not vary during the protostellar evolution, consistent with C 18 O observations obtained with Herschel and from ground-based telescopes. The NIR spectra provide complementary information to the submm lines by probing not only the cold outer envelope but also the warm inner region. The NIR high-J (≥8) absorption lines are particularly sensitive to the physical structure of the inner few AU, which does show evolution. The models indicate that observations of 13 CO and C 18 Ol ow-J submm lines within a ≤1 �� (at 140 pc) beam are well suited to probe embedded disks in Stage I (Menv < M� ) sources, consistent with recent interferometric observations. High signal-to-noise ratio subarcsec resolution data with ALMA are needed to detect the presence of small rotationally supported disks during the Stage 0 phase and various diagnostics are discussed. The combination of spatially and spectrally resolved lines with ALMA and at NIR is a powerful method to probe the inner envelope and disk formation process during the embedded phase.
Energy Spectra of Vortex Distributions in Two-Dimensional Quantum Turbulence

We theoretically explore key concepts of two-dimensional turbulence in a homogeneous compressible superfluid described by a dissipative two-dimensional Gross-Pitaeveskii equation. Such a fluid supports quantized vortices that have a size characterized by the healing length � . We show that, for the divergencefree portion of the superfluid velocity field, the kinetic-energy spectrum over wave number k may be decomposed into an ultraviolet regime (k � � � 1 ) having a universal k � 3 scaling arising from the vortex core structure, and an infrared regime (k � � � 1 ) with a spectrum that arises purely from the configuration of the vortices. The Novikov power-law distribution of intervortex distances with exponent � 1=3 for vortices of the same sign of circulation leads to an infrared kinetic-energy spectrum with a Kolmogorov k � 5=3 power law, which is consistent with the existence of an inertial range. The presence of these k � 3 and k � 5=3 power laws, together with the constraint of continuity at the smallest configurational scale k � � � 1 , allows us to derive a new analytical expression for the Kolmogorov constant that we test against a numerical simulation of a forced homogeneous, compressible, two-dimensional superfluid. The numerical simulation corroborates our analysis of the spectral features of the kinetic-energy distribution, once we introduce the concept of a clustered fraction consisting of the fraction of vortices that have the same sign of circulation as their nearest neighboring vortices. Our analysis presents a new approach to understanding two-dimensional quantum turbulence and interpreting similarities and differences with classical twodimensional turbulence, and suggests new methods to characterize vortex turbulence in two-dimensional quantum fluids via vortex position and circulation measurements.
https://doi.org/10.18697/ajfand.112.216405-A CASH CROP COMBINATION FOR MAXIMUM NET INCOME: A CASE STUDY OF A SMALL-SCALE CASH CROP FARMER IN VHEMBE, LIMPOPO PROVINCE, SOUTH AFRICA

This paper investigates the optimal cash crop combination at a small-scale cash crop farm in Vhembe District, Limpopo Province, South Africa in which green maize, cabbages, tomatoes, spinach, mustard, butternut and sweet potatoes are grown. To get optimum farm outputs, decisions on crop combination and operational activities in crop production are crucial. Proper farm planning and resource allocation play a significant role in optimising farm revenues. It was observed that the farmer used traditional methods of allocating resources, which lead to a less profitable crop mix. In view of this, in this study, linear programming model was formulated using data collected from a farm concerning the past crop combinations and allocation of resources in crop planning and production to determine the best crop combination that maximizes net income given limited resources such as land, labour, capital, and others. The simplex method of linear programming works by first locating a feasible solution and then relocating to any vertex of the feasible set that improves the cost function. Eventually, a point is reached beyond which no further movement improves the cost function. The results of the developed linear programming model were compared to past farming practices based on experience, leading to the conclusion that crops and limited resources were not optimally allocated. The results clearly demonstrate the optimal crop combination and allocation of scarce resources that the farm could have considered to yield maximum returns. It is observed that the proposed linear programming model is appropriate for finding the optimal land allocation criteria for the cash crops in the study area. The optimal crop mix from the linear programming two phase simplex method show that the farmer should grow the following crop mix, 1.16 ha of green mealies, 2.64 ha of cabbages, 0.8 ha of tomatoes, 1.2 ha of mustard, 0.4 ha spinach, 0.4 ha of butternut and 0.4 ha of sweet potatoes with the gross income of R 740 800. The linear programming model resulted in a 37.8 percent increase in profit margin. Based on the results obtained from this study, it is recommended that the small-scale cash crop farmer should invest more in producing crops that give maximum profit. MATLAB software was used to determine the optimal values of the decision variables. resource allocation. combination and resource allocation problem which is solved using linear programming model focussing on a case study of a small-scale cash crop farmer in Vhembe District, Limpopo Province, South Africa where different farming activities are carried out with some resources in short supply and the farm targets to get maximum production and returns. There is no such study known that has been conducted in this province. The results from this study can be extended to farms of different sizes and encourage the use of linear programming model in informed decision making process.
Developing a New Parameterization Scheme of Temperature Lapse Rate for the Hydrological Simulation in a Glacierized Basin Based on Remote Sensing

Temperature lapse rate (TLR) is an important parameter for simulations of snow/glacier melts in alpine watersheds. However, the traditional scheme estimates TLR mainly based on a limited number of stations and may not be suitable for alpine watersheds with scarce observations. To overcome this problem, this study developed a new basin-oriented TLR scheme based on Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature data which was applied in a typical glacierized watershed on the northern slope of the Himalayas. The new TLR scheme was evaluated by comparison with the station-based traditional TLR scheme in terms of performance in simulations of both snow cover area and runoff using the same hydrological model. The results showed that the monthly TLRs estimated by the new scheme presented a more reasonable seasonal pattern than those estimated by the traditional scheme. The model using the new TLR scheme showed better performance in discharge simulations of low-runoff months than that using the traditional TLR scheme. The R-squared value of multiyear mean monthly snow cover area of the former (0.82) was also higher than that of the latter (0.56). This study provides an efficient framework for estimating TLR based on remote sensing for alpine watersheds with scarce observations.
A Bibliometric and Visual Analysis of Exercise Intervention Publications for Alzheimer’s Disease (1998–2021)

Alzheimer’s disease (AD) is the most common cause of dementia worldwide, posing a considerable economic burden to patients and society as a whole. Exercise has been confirmed as a non-drug intervention method in the related literature on AD. However, at present, there are still few bibliometric studies on AD exercise research. In order to fill the gap, this paper aims to intuitively analyze the growth in AD exercise literature published from 1998 to 2021 using bibliometrics, providing historical insights for scientific research circles. The main source of literature retrieval is the Web of Science database. Using the Boolean operator tools “OR” and “AND” combined with keywords related to “exercise” and “Alzheimer’s disease”, we conducted a title search and obtained 247 documents. Using Microsoft Excel, Datawrapper, and Biblioshiny, this study carried out a bibliometric analysis of countries, institutions, categories, journals, documents, authors, and keyword plus terms. The study found that the number of papers published from 2016 to 2021 had the greatest increase, which may have been influenced by the Global Dementia Report 2015 and COVID-19. Interdisciplinary cooperation and the research results published in high-scoring journals actively promoted research and development in the AD exercise field. The United States and the University of Minnesota system play a central role in this field. In future, it will be necessary to explore the effectiveness and feasibility of multi-mode interventions on an active lifestyle, including exercise, in different groups and environments worldwide. This study may provide a direction and path for future research by showing the global overview, theme evolution, and future trends of research results in the AD exercise field.
THE MASS OF THE CENTRAL BLACK HOLE IN THE NEARBY SEYFERT GALAXY NGC 5273

We present the results of a reverberation-mapping program targeting NGC 5273, a nearby early-type galaxy with a broad-lined active galactic nucleus (AGN). Over the course of the monitoring program, NGC 5273 showed strong variability that allowed us to measure time delays in the responses of the broad optical recombination lines to changes in the continuum flux. A weighted average of these measurements results in a black hole mass determination of MBH = (4.7 ± 1.6) × 106 M☉. An estimate of the size of the black hole sphere of influence in NGC 5273 puts it just at the limit of the resolution achievable with current ground-based large aperture telescopes. NGC 5273 is therefore an important future target for a black hole mass determination from stellar dynamical modeling, especially because it is the only nearby early-type galaxy hosting an AGN with a reverberation-based mass, allowing the best comparison for the masses determined from these two techniques.
ImageNet-D: A new challenging robustness dataset inspired by domain adaptation

We propose a new challenging dataset to benchmark robustness of ImageNet-trained models with respect to domain shifts: ImageNet-D. ImageNet-D has six different domains (“Real”, “Painting”, “Clipart”, “Sketch”, “Infograph” and “Quick-draw”). We show that even state-of-the-art models struggle on this dataset and find that they make well-interpretable errors. For example, our best EfficientNet-L2 model experiences a large performance drop even on the “Real” domain from 11.6% on ImageNet clean to 29.2% on the “Real” domain. Robustness datasets on ImageNet-scale (IN, Deng et al., 2009) have so far been limited to a few selected domains— image corruptions in ImageNet-C (IN-C, Hendrycks & Di-etterich, 2019), image renditions in ImageNet-R (IN-R, Hendrycks et al., 2020a), difficult images for ResNet50 (He et al., 2016) classifiers in ImageNet-A (IN-A, Hendrycks et al., 2019) or unusual viewpoints in ObjectNet (Barbu et al., 2019). To enable researchers to benchmark their models on a wider range of complex distribution shifts, we re-purpose the dataset from the Visual Domain Adaptation Challenge 2019 (DomainNet, Saenko et al., 2019) as an additional robustness benchmark. This dataset comes with six image styles: Clipart, Real, Infograph, Painting, Quick-draw and Sketch. To benchmark robustness of IN-trained models out of the box, we filter out the classes that cannot be mapped to IN and refer to the smaller version of Domain-Net as ImageNet-D (IN-D). We show example images from IN-D in Fig.
The Influence of Diet and Physical Activity on Oxidative Stress in Romanian Females with Osteoarthritis

Osteoarthritis (OA) is the most prevalent chronic joint disease, increases in prevalence with age, and affects most individuals over 65. The present study aimed to assess the oxidative status in relation to diet and physical activity in patients with OA. We used a cross-sectional study applied to 98 females with OA. Blood samples were collected to determine oxidative stress markers: malonyl dialdehyde (MDA), reduced glutathione (GSH), oxidized glutathione (GSSG), and GSH/GSSG. Diet was estimated with a standardized food frequency questionnaire. We used the International Physical Activity Questionnaire (IPAQ) to assess the females’ physical activity. Multiple regression analyses were executed to determine the association between the oxidative markers and the intake of vegetables and fruit. The study showed that most patients were overweight or obese (88.8%). The level of physical activity was above the recommended level for adults, mainly based on household activities. The intake of vegetables and fruit was low. The MDA marker was inversely, statistically significantly associated with the consumption of vegetables (p < 0.05). Public health policies must address modifiable risk factors to reduce energy intake and obesity and increase the intake of vegetables and fruit. Higher consumption of vegetables and fruit may provide natural antioxidants that can balance oxidative compounds.
Efficient Task Adaptation by Mixing Discovered Skills

Unsupervised skill discovery is one of the approaches by which the agent learns potentially useful and distinct behaviors without any explicit reward. The agent is then expected to quickly solve downstream tasks by properly using a set of discovered skills rather than learning everything from scratch. However, it is non-trivial to optimally utilize the discovered skills for each task, which can be viewed as a fine-tuning method, and this has been less considered in the literature in spite of its importance. In this paper, we compare some fine-tuning methods showing how they inefficiently utilize the discovered skills and also propose new methods, which are sample-efficient and effective by interpreting the skills as a perspective of how an agent transforms the input state. Our code is available at https://github.com/jsrimr/unsupervisedRL.
Humoral Response to Hepatitis B and COVID-19 Vaccine among Maintenance Hemodialysis Patients

Maintenance hemodialysis (MHD) patients have impaired immunological responses to pathogens and vaccines. In this study, we compared the humoral response to HBV and COVID-19 vaccines in a cohort of MHD patients. Demographic and clinical characteristics of vaccine responders and non-responders were also compared, and the association between the humoral responses to both vaccines was evaluated. The cohort included 94 MHD patients who were vaccinated at least once for HBV and twice for COVID-19. Among the 94 patients, 28 (29.8%) did not develop protective titers to HBV. Hypertension, coronary heart disease, and heart failure were more common in non-responders. Among MHD patients, 85% had positive IgG anti-spike SARS-CoV-2 levels 6 months after two doses of BNT162b2 (Pfizer/Biotech) vaccine. Age and immunosuppressive therapy were the main predictors of humoral response to COVID-19 vaccine. We did not find any association between non-responders to HBV and non-responders to COVID-19 vaccine. There was no difference in IgG anti-spike titers between HBV responders and non-responders (505 ± 644 vs. 504 ± 781, p = 0.9) Our results suggest that reduced humoral response to hepatitis B is not associated with reduced response to COVID-19 vaccine. Different risk-factors were associated with poor immune response to HBV and to COVID-19 vaccines.
Investment and ﬁrm dynamics

In this paper I ask whether a model of ﬁrm capital accumulation with entry and exit calibrated to match the investment regularities of U.S. establishments is capable of generating the dependence of ﬁrm dynamics on size and age. Firms face uncertainty in the form of idiosyncratic productivity shocks and are subject to non-convex capital adjustment costs. I solve for the stationary equilibrium to show that the model can account for the simultaneous dependence of industry dynamics on size (once we condition on age) and on age (once we condition on size).
A New Data Processing System for Generating Sea Ice Surface Roughness Products from the Multi-Angle Imaging SpectroRadiometer (MISR) Imagery

Sea ice roughness can serve as a proxy for other sea ice characteristics such as ice thickness and ice age. Arctic-wide maps that represent spatial patterns of sea ice roughness can be used to better characterize spatial patterns of ice convergence and divergence processes. Sea ice surface roughness can also control and quantify turbulent exchange between sea ice surface and atmosphere and therefore influence surface energy balance at the basin scale. We have developed a data processing system that produces georeferenced sea ice roughness rasters that can be mosaicked to produce Arctic-wide maps of sea ice roughness. This approach starts with Top-of-Atmosphere radiance data from the Multi-angle Imaging SpectroRadiometer (MISR). We used red-band angular data from three MISR cameras (Ca, Cf, An). We created a training data set in which MISR pixels were matched with co-located and concurrent lidar-derived roughness measurements from the Airborne Topographic Mapper (ATM). We used a K-nearest neighbor algorithm with the training data to calibrate the multi-angle data to values of surface roughness and then applied the algorithm to Arctic-wide MISR data for two 16-day periods in April (spring) and July (summer). After georeferencing the roughness rasters, we then mosaicked each 16-day roughness dataset to produce Arctic-wide maps of sea ice roughness for spring and summer. Assessment of the results shows good agreement with independent ATM roughness data, not used in model development. A preliminary exploration of spatial and seasonal changes in sea ice roughness for two locations shows the ability to characterize the roughness of different ice types and the results align with previous studies. This processing system and its data products can help the sea ice research community to gain insights into the seasonal and interannual changes in sea ice roughness over the Arctic.
A DETAILED STUDY OF PHOTOMETRIC REDSHIFTS FOR GOODS-SOUTH GALAXIES

We use the deepest and the most comprehensive photometric data currently available for GOODS-South (GOODS-S) galaxies to measure their photometric redshifts. The photometry includes VLT/VIMOS (U band), HST/ACS (F435W, F606W, F775W, and F850LP bands), VLT/ISAAC (J, H, and Ks bands), and four Spitzer/IRAC channels (3.6, 4.5, 5.8, and 8.0 μm). The catalog is selected in the z band (F850LP) and photometry in each band is carried out using the recently completed TFIT algorithm, which performs point-spread function (PSF) matched photometry uniformly across different instruments and filters, despite large variations in PSFs and pixel scales. Photometric redshifts are derived using the GOODZ code, which is based on the template fitting method using priors. The code also implements “training” of the template spectral energy distribution (SED) set, using available spectroscopic redshifts in order to minimize systematic differences between the templates and the SEDs of the observed galaxies. Our final catalog covers an area of 153 arcmin2 and includes photometric redshifts for a total of 32,505 objects. The scatter between our estimated photometric and spectroscopic redshifts is σ = 0.040 with 3.7% outliers to the full z-band depth of our catalog, decreasing to σ = 0.039 and 2.1% outliers at a magnitude limit mz< 24.5. This is consistent with the best results previously published for GOODS-S galaxies, however, the present catalog is the deepest yet available and provides photometric redshifts for significantly more objects to deeper flux limits and higher redshifts than earlier works. Furthermore, we show that the photometric redshifts estimated here for galaxies selected as dropouts are consistent with those expected based on the Lyman break technique.