Miracle-dz/UniFAR-FTUs huggingface dataset:
A new stellar library in the K band for the empirical calibration of the CO index

Abstract Here we present the results of an observational program aimed at providing a stellar library in the K band with an appropriate coverage of physical stellar parameters (effective temperature, gravity and metallicity) to be used for stellar population synthesis models. In particular, we study the behavior of the CO feature at 2.3 μm as a function of the stellar parameters and we will compute empirical fitting functions that can be easily implemented into stellar population models to provide accurate predictions for integrated CO strengths that will help to face outstanding problems in galaxy formation and evolution.
Globally Increasing Atmospheric Aridity Over the 21st Century

Vapor pressure deficit (VPD) is of great importance to control the land‐atmosphere exchange of water and CO2. Here we use in situ observations to assess the performance of monthly VPD calculated from state‐of‐the‐art data sets including CRU, ERA5, and Modern‐Era Retrospective analysis for Research and Applications, Version 2 (MERRA2). We investigate trends in VPD at global scale and for different climatic zones for 1981–2020 and future trends (2021–2100) from Coupled Model Inter‐comparison Project phase 6 (CMIP6) outputs. The results show that monthly VPD estimated from CRU, ERA5, and MERRA2 correlated well against in situ estimates from 15,531 World Meteorological Organization stations, with R2 ranging between 0.92 and 0.96. Moreover, robust correlations were also found across in situ stations and when analyzing different months separately. During 1981–2020, VPD increased in all climatic zones, with the strongest increase in the arid zone, followed by tropical, temperate, cold and polar zones. CMIP6 simulations show a continuously increasing trend in VPD (0.028 hPa year−1), with the largest increase in the arid zone (0.063 hPa year−1). The magnitudes of trends are found to increase following the magnitude of CO2 increases in the future emission scenarios. We highlight that atmospheric aridification will continue under global warming, which may pose an increasing threat to terrestrial ecosystems and particularly dryland agricultural systems.
FORMATION OF COMPACT STELLAR CLUSTERS BY HIGH-REDSHIFT GALAXY OUTFLOWS. I. NON-EQUILIBRIUM COOLANT FORMATION

We use high-resolution three-dimensional adaptive mesh refinement simulations to investigate the interaction of high-redshift galaxy outflows with low-mass virialized clouds of primordial composition. While atomic cooling allows star formation in objects with virial temperatures above 104 K, “minihalos” below this threshold are generally unable to form stars by themselves. However, these objects are highly susceptible to triggered star formation, induced by outflows from neighboring high-redshift starburst galaxies. Here, we conduct a study of these interactions, focusing on cooling through non-equilibrium molecular hydrogen (H2) and hydrogen deuteride (HD) formation. Tracking the non-equilibrium chemistry and cooling of 14 species and including the presence of a dissociating background, we show that shock interactions can transform minihalos into extremely compact clusters of coeval stars. Furthermore, these clusters are all less than ≈106 M☉, and they are ejected from their parent dark matter halos: properties that are remarkably similar to those of the old population of globular clusters.
Car Sharing in Marketing

The objective of this article is to explore several studies on Car Sharing (CS) in the marketing discipline. A total of 1,251 articles was collected from several journals in the Q1 Schimago category. Of these number 165 articles were chosen for research goals. A detailed investigation reduced this amount to 50 articles. The content analysis technique is applied by examining some key aspects, such as theme, authorship, research type, scope, and research design. The results of this study show that top-class journals have not published many articles about CS in the marketing discipline, but this does show an upward trend. Most articles are empirical papers. In general, the findings show a shift in the focus of CS studies in the marketing discipline over time. Keywords—Online transportation; Sharing economy; Car sharing; Marketing; Content analysis
Comparative analysis of machine learning based classification for abiotic stress proteins

For thousands of years, cereals which include rice, wheat, maize, sorghum and millets etc. have been playing major role in human civilization. These are the principal components of human diet and important staples for daily survival of billions of people globally. The cereal crops belong to poaceae family and rich in vitamins, minerals and fiber. They are reported to reduce the coronary heart disease and other serious diseases. These crops are adversely affected by biotic and abiotic stresses like cold, drought, heat and salinity. With the advent of modern NGS technologies, the plethora of molecular data leads to infer many unexplored facts of the cereal crops using in-silico approach. In the present work, computational techniques were applied to study thoroughly the classification of abiotic stresses (cold, drought, heat and salinity) responsive genes in cereals. The datasets of four stress responsive genes in poaceae family was retrieved from public domain. The machine learning based methodologies namely, Random forest, Support Vector Machines and Deep Learning-Long Short-Term Memory (DL-LSTM) were applied. A comparative analysis was carried out for classification of the retrieved data with k-fold cross validation applying the machine learning techniques at different parameters. It was observed that for all the four sets of data, accuracy was maximum, i.e. 95.11%, 76.88%, 94.31% and 82.04% for cold, drought, heat and salinity, respectively using DL-LSTM. Comparison of the methodologies obviates the outperformance of deep leaning. Such approach of computational studies will help researchers to study the complex biological problems of gene classification more efficiently.
Financial Innovation, Financial Patents and Business Performance: An Empirical Study on the Banking Industry in Taiwan

There have been a lot of discussions on financial patents abroad, while the number of domestic fintech patent applications is still small. In view of the past research on financial innovation and financial patents on company performance, the results are inconsistent. Therefore, in this study, a panel data model of the impact of financial innovation and financial patents on business performance in Taiwan's banking industry from 2005 to 2020 is constructed, and the performance determinants of financial innovation and financial patents are further explored. This study also uses the data envelopment analysis (DEA) to distinguish between those who have invested in financial patents and those who have not and compares the difference between their efficiency. Finally, the sample is divided into public and private banks, and financial holding and non-financial holding banks to further examine the differences in the influencing factors of the sub-samples. The results show that non-financial holding banks continue to improve their technical efficiency to keep up with the fierce competition in the banking industry.
Thermal Casimir effect in the interaction of graphene with dielectrics and metals

We investigate the thermal Casimir interaction of a suspended graphene described by the Dirac model with a plate made of dielectric or metallic materials. The reflection coefficients on graphene expressed in terms of a temperature-dependent polarization tensor are used. We demonstrate that for a graphene with nonzero mass gap parameter the Casimir free energy remains nearly constant (and the thermal correction negligibly small) over some temperature interval. For the interaction of graphene with metallic plate, the free energy is nearly the same, irrespective of whether the metal is nonmagnetic or magnetic and whether it is described using the Drude- or plasma-model approaches. The free energy computed using the Dirac model was compared with that computed using the hydrodynamic model of graphene and big differences accessible for experimental observation have been found. For dielectric and nonmagnetic metallic plates described by the Drude model these differences vanish with increasing temperature (separation). However, for nonmagnetic metals described by the plasma model and for magnetic metals, a severe dependence on the chosen theoretical description of graphene remains even at high temperature. In all cases the analytic asymptotic expressions for the free energy at high temperature are obtained and found in a very good agreement with the results of numerical computations.
The Impact of Workplace Violence on Headache and Sleep Problems in Nurses

Workplace violence (WV) is a significant occupational hazard for nurses. Previous studies have shown that WV has a reciprocal relationship with occupational stress. Headaches and sleep problems are early neuropsychological signs of distress. This cross-sectional study aims to ascertain the frequency of physical or verbal assaults on nurses and to study the association of WV with headaches and sleep problems. During their regular medical examination in the workplace, 550 nurses and nursing assistants (105 males, 19.1%; mean age 48.02 ± 9.98 years) were asked to fill in a standardized questionnaire containing the Violent Incident Form (VIF) concerning the episodes of violence experienced, the Headache Impact Test (HIT-6) regarding headaches, and the Pittsburgh Sleep Quality Inventory (PSQI) on sleep quality. Occupational stress was measured using the Effort/Reward Imbalance questionnaire (ERI). Physical and non-physical violence experienced in the previous year was reported by 7.5% and 17.5% of workers, respectively. In the univariate logistic regression models, the workers who experienced violence had an increased risk of headaches and sleep problems. After adjusting for sex, age, job type, and ERI, the relationship between physical violence and headaches remained significant (adjusted odds ratio aOR = 2.25; confidence interval CI95% = 1.11; 4.57). All forms of WV were significantly associated with poor sleep in a multivariate logistic regression model adjusted for sex, age, job type, and ERI (aOR = 2.35 CI95% = 1.44; 3.85). WV was also associated with the impact of headaches and with sleep quality. WV prevention may reduce the frequency of lasting psychoneurological symptoms, such as headaches and poor sleep quality, that interfere with the ability to work.
Iterative grating interferometry-based phase-contrast CT reconstruction with a data-driven denoising prior

Breast cancer is the most common malignancy in women. Unfortunately, even though screening programs have helped to increase survival rates, the number of false positives and false negatives remains high. Phase-contrast X-ray CT is a promising imaging technique which could improve breast cancer diagnosis by combining the high three-dimensional resolution of conventional CT with higher soft-tissue contrast. Grating Interferometry CT (GI-CT) arguably has the highest chance to make the transition to clinical practice. Unfortunately though, obtaining high-quality images is challenging. Grating fabrication defects and photon starvation lead to high noise amplitudes in the measured data. Moreover, the highly ill-conditioned differential nature of the GI-CT forward operator renders the inversion from corrupted data even more cumbersome. In this article we report on a novel regularized iterative reconstruction algorithm with a powerful data-driven regularization strategy to tackle this challenging inverse problem. In particular, we present an algorithm that combines the L-BFGS optimization scheme with a Plug-and-Play denoiser parameterized by a deep neural network and empirically show that the proposed method achieves high quality images, both on simulated data as well as on real measurements.
Parallel algorithms for solution of nonlinear diffusion problems in image smoothing 1

Abstract In this work we consider parallel algorithms for solution of nonlinear parabolic PDEs. First mathematical models describing nonlinear diffusion filters are presented. The finite‐volume method is used to approximate differential equations. Parallel algorithms are based on the domain decomposition method. The algorithms are implemented by using ParSol parallelization tool and a brief description of this tool is also presented. The efficiency of proposed parallel algorithms is investigated and results of the scalability analysis are given. Theoretical predictions are compared with results of computational experiments. Application of nonlinear diffusion filters for analysis of computer tomography images is discussed in the last section of the paper.
Growth and Distribution of Boron in Oilseed Rape (Brassica napus L.) as Affected by Boron Supply

Oilseed rape (Brassica napus L.) is one of the most important oilseed crops. It has relatively high boron (B) requirements for growth. In this study, a hydroponic experiment was performed to determine the critical B requirement and B distribution in B. napus. The plants were grown for four weeks at a range of B levels (from 0.25 to 1000 µM) supplied in a nutrient solution. The results showed significant differences in the root and shoot dry matter and B accumulation in these tissues among the supplied B levels. Severe visible symptoms of B deficiency were observed on the leaves at levels lower than 1 µM B and toxicity at 1000 µM B in the nutrient solution. The maximum shoot and root dry matter were recorded at 25 µM B in the nutrient solution. The plants supplied with the lowest and the highest B levels produced 35% and 37% less shoot dry matter than those supplied with 25 µM B, while the corresponding decreases in the root dry matter were 48% and 36%, respectively. The critical concentration of B, which is the lowest concentration at which plants produce 90% of the maximum shoot dry matter, was proven to be 1 µM B for oilseed rape. At this level of external B supply, the B concentration in the shoot was 26.9 mg kg−1 DM. It was found that with the increase in B levels in the nutrient solution, the relative distribution of B between the roots and the shoots shifted in favor of the shoots.
Detection of standing retention trees in boreal forests with airborne laser scanning point clouds and multispectral imagery

In a landscape consisting primarily of intensive forestry interspersed with some protected areas, multifunctional forestry with retention trees can play a crucial role in nature conservation. Accurate mapping of retention trees is important for guiding landscape‐level conservation and forest management and improving landscape connectivity. Sizeable dead and living retention trees play a particularly important ecological role but even their large‐scale inventory is often intensive through field work and/or inaccurate. We aimed to detect and classify retention trees using the novel nationwide Finnish airborne laser scanning (ALS) data (~5 pulses/m2) in conjunction with unrectified colour‐infrared (CIR) aerial imagery. Applying photogrammetric principles, we added spectral information from the CIR imagery to the ALS‐derived point cloud. For a training dataset of 160 retention trees from 19 stands and a geographically separate validation dataset of 79 trees from eight stands, we segmented trees via individual tree detection (ITD), removed most trees belonging to the regenerating vegetation layer, and classified trees into living conifers, living broadleaves and dead trees by linear discriminant analysis. The detection rate via ITD differed considerably for dead and living trees, with 41.7% of all dead and 83.8% of all living trees being detected with relatively low commission error rates. Dead trees with smaller diameters and heights were more likely missed, while grouping caused living tree omission. For classification into living conifers, living broadleaves and dead trees, an overall accuracy of 67.3% was achieved in training and 71.2% in validation using only ALS‐derived metrics. When adding spectral metrics, the overall accuracies were 79.6% and 61.0% for training and validation respectively. Our findings imply that wall‐to‐wall large‐scale high density ALS data can be used to detect retention trees rather accurately—even larger dead trees—and that metrics derived solely from ALS data can accurately classify detected retention trees into living conifers, living broadleaves and dead trees. Considering the ecological value of retention trees, our results are promising and indicate that ALS data of the studied pulse density are a cost‐effective option for large area mapping of retention trees in countries with such data available.
Drinking coffee may reduce chances of developing Alzheimer’s disease: systematic literature review and meta-analysis

Coffee is a popular beverage, and it contains caffeine, a psychoactive substance. Consuming coffee may reduce the risk of developing Alzheimer’s disease (AD). However, the association between the reduced risk of developing AD and the consumption of coffee is controversial. Therefore, we conducted a systematic literature review and quantitative synthesis meta-analysis that included dose-response analysis on the relationship between the consumption of coffee and the risk of developing AD. Based on PRISMA guidelines, we analysed standard databases of journals published between January 1999 and May 2020. We included the two population-based cohort studies and one case-control study. All studies included looked at the association between consuming many cups of coffee, the amount of coffee consumed in milligrams per day and the risk of developing AD. The systematic literature review and meta-analysis had 1670 participants with follow-up years that ranged from 5 to 21. The consumption of moderate or 3-5 cups per day reduces the risk of developing AD. The pooled relative risk and 95% confidence interval of the 3 included studies were 0.63 (0.3, 1.54). Dose-response curve analysis appears to be U-shaped. The results of the forest plot showed that there is low heterogeneity between the studies. Plotting the funnel plot and the Galbraith plot demonstrated publication bias of the three included studies. More prospective and long-term studies have to be conducted in other countries to determine the exact risk of developing AD.
Modelling the Impacts of Social Value on Platform Identity: The Mediating-Moderating Research Design in Live-Streaming Context

In the era of digitalization and epidemic, more people deeply rely on digital tools. Digital tools can be used to complete transactions for food delivery, daily necessities and car booking. Live broadcasting is one of the representative products in the digital era. Users’ perceived social value and its impacts are important to the society and operators of platform. Fewer studies, however, have verified the relationships among social value, platform dependence, and platform identity. Accordingly, the research purpose of this paper is to analyze users' digital inertia, social values, and the relationships among their dependence and their identity on livestreaming platforms. Data was collected by questionnaire survey and 372 valid samples were received. Findings show that in the relationships between live streaming platforms and users, positive relationships among variables were met; the social value perceived by the users could affect platform identity through platform dependence; and users' perceived digital inertia significantly could moderate the relationship between social value and platform dependence. It is suggested that operators should strengthen users' perception of social value when designing live broadcast activities.
The Social Distance Impacts from COVID-19 Pandemic on the Development of Two Orders of a Concurrent Training Programme for Morbidly Obese Patients

Background: Although there is relevant information regarding the consequences of the coronavirus SARS-CoV-2 (COVID-19), little is known about the impact of the imposed social confinement (at home) on the development of exercise training programmes in populations with morbid obesity. Aim: To describe the effects of the imposed COVID-19 confinement on the cardiometabolic health benefits acquired through a concurrent training programme that started before the pandemic in populations with morbid obesity. Methods: This was an experimental randomized clinical study, in which sedentary morbidly obese women were assigned 1:1 to a high-intensity interval training (HIIT) plus resistance training (RT) group (HIIT + RT; n = 11; BMI 42.1 ± 6.6) or to the same exercise dose, but in different order group of RT plus HIIT group (RT + HIIT; n = 7; BMI 47.5 ± 8.4). Both groups undertook two sessions/week. When COVID-19 confinement at home started, a post-test was applied in January 2020 (Post1) and after 20 months (Post2). The main outcomes were waist circumference (WC), systolic (SBP) and diastolic blood pressure (DBP), high-density lipids (HDL-c), triglycerides (Tg), and fasting plasma glucose (FPG). Results: In the HIIT + RT group, the WC showed significant increases from Post1 to Post2 (Δ + 3.1 cm, p = 0.035); in the RT + HIIT group, it decreased from Post1 to Post2 (Δ − 4.8 cm, p = 0.028). In the HIIT + RT group, SBP showed significant increases from Post1 to Post2 (Δ + 6.2 mmHg, p = 0.041); the RT + HIIT group decreased SBP from Pre0 to Post1 (Δ − 7.2 mmHg, p = 0.026) and increased DBP from Pre0 to Post1 (Δ + 8.1 mmHg, p = 0.015). Tg in the HIIT + RT group decreased from Pre0 to Post1 (Δ − 40.1 mg/dL, p = 0.023) but increased from Post1 to Post2 (Δ + 86.3 mg/dL, p < 0.0001). Conclusions: The COVID-19 social confinement worsened metabolic syndrome (MetS) outcomes that had improved from 20 weeks’ RT + HIIT during the training period, such as WC, SBP, and Tg from HIIT + RT, when, worryingly, SBP increased to another more serious clinical classification in both groups.