Miracle-dz/UniFAR-FTUs huggingface dataset:
Yield and Yield Components of Potato (Solanum tuberosum) as Affected by Rock Phosphate in Standoff Soil, Southern Alberta Canada

An experiment was conducted in Standoff, Southern Alberta in April, 2020. The object of the experiment was to investigate effect of rock phosphate organic fertilizer on growth and yield of potato crop grown in Standoff. The varying levels of rock phosphate were broadcasted into the soil at control (0 P Kg ha-1), Low P level (50 P Kg ha-1) and High P level (100 P Kg ha-1). The basal application of urea inform of nitrogen fertilizer was applied at 280 N Kg ha-1. Potato seeds were planted at a distance of 30 by 90 cm. The three treatments were replicated three times, resulting into nine plants. One plant was taken out of uniformly grown tallest plant in each of the treatment to measure yield parameters. The yield parameters collected were subjected to analysis of variance (ANOVA) using Duncan’s Multiple Range Test (DMRT) for separation of means. Results of the experiment indicated that High P and Low P rock phosphate fertilizer levels positively influenced weight of potatoes at 76 and 112 Days after sowing (DAS), respectively while High P rock phosphate fertilizer level got highest number of potatoes than Low P and control at 76 DAS. Furthermore, High P rock phosphate fertilizer level and control plots supported marketable number of potatoes at 76 DAS while High P rock phosphate fertilizer level favoured unmarketable number of potatoes at 112 DAS. It was quite obvious from the results that marketable weight of potatoes was positively influenced by High P rock phosphate level and Low P rock phosphate level at 76 and 112 DAS, respectively whereas unmarketable weight of potatoes was affected by High P rock phosphate fertilizer level at 112 DAS. These results revealed the beneficial use of rock phosphate for potato crop production
Integration of Fault Rupture Models in Seismic Hazard Analysis. A Perspective from the Consultancy Side

Seismic hazard analysis (SHA) is an essential element in the site characterisation of a nuclear installation. A SHA comprises two major tasks: Seismic Source Characterisation (SSC) and Ground Motion Characterisation (GMC). Their respective outputs constitute the input for the hazard analysis, which includes obtaining ground motions at a reference rock, calculating the site response, and deriving compatible time histories. The paper identifies, from the perspective of an engineering consultant, the aspects of a SHA that could benefit from the use of fault rupture models, analysing the advantages, identifying their costs, and bringing up the points that need to be analysed to establish their relevance. Traditionally the GMC output has been a set of Ground Motion Prediction Equations (GMPEs). There are already GMPEs whose database has been completed with ground motions derived from simulations, which is a first (indirect) presence of fault rupture modelling in SHAs. The key question is whether generic GMPEs could be replaced by ad hoc fault rupture models in SHAs for the nuclear industry. Some ideas and even practical applications can already be found in the technical literature and in technical documents issued by official organisations like the IAEA. The site response analysis and the generation of time histories, both usually uncoupled from the rest of the analyses, would probably be more easily integrated in the overall process when employing fault rupture models. All these benefits would entail costs. Firstly, there is an obvious increase in the computational cost; second, the SHA team needs to be more multidisciplinary, enlisting specialists in fault rupture modelling, although this may be more an investment than a cost. A SHA must take uncertainties into account. When deciding about the relevance of fault rupture models, the previously detected pros and cons have to be weighted together with the level of uncertainty of the input parameters and the impact on the reliability of the results.
Using Machine Learning and Qualitative Interviews to Design a Five-Question Women's Agency Index

We propose a new method to design a short survey measure of a complex concept such as women's agency. The approach combines mixed-methods data collection and machine learning. We select the best survey questions based on how strongly correlated they are with a "gold standard" measure of the concept derived from qualitative interviews. In our application, we measure agency for 209 women in Haryana, India, first, through a semi-structured interview and, second, through a large set of close-ended questions. We use qualitative coding methods to score each woman's agency based on the interview, which we treat as her true agency. To identify the close-ended questions most predictive of the "truth," we apply statistical algorithms that build on LASSO and random forest but constrain how many variables are selected for the model (five in our case). The resulting five-question index is as strongly correlated with the coded qualitative interview as is an index that uses all of the candidate questions. This approach of selecting survey questions based on their statistical correspondence to coded qualitative interviews could be used to design short survey modules for many other latent constructs.
Estimation of the Ex Ante Distribution of Returns for a Portfolio of U.S. Treasury Securities Via Deep Learning

This paper presents different deep neural network architectures designed to forecast the distribution of returns on a portfolio of U.S. Treasury securities. A long short-term memory model and a convolutional neural network are tested as the main building blocks of each architecture. The models are then augmented by cross-sectional data and the portfolio's empirical distribution. The paper also presents the fit and generalization potential of each approach.
Non-linearities and persistence in US long-run interest rates

ABSTRACT This note examines the stochastic behaviour of US monthly 10-year government bond yields. Specifically, it estimates a fractional integration model suitable to capture both persistence and non-linearities, these being two important properties of interest rates. Two series are analysed, one from Bloomberg including end-of-the-month values over the period January 1962-August 2020, the other from the ECB reporting average monthly values over the period January 1900-August 2020. The estimation results indicate that both are highly persistent and exhibit non-linearities, the latter being more pronounced in the case of the ECB series. Also, there is no conclusive evidence of the presence of structural breaks.
Expectation Dispersion, Uncertainty, and the Reaction to News

Releases of key macroeconomic indicators are closely watched by financial markets. We investigate the role of expectation dispersion and economic uncertainty for the stock-market reaction to indicator releases. We find that the strength of the financial market response to news decreases with the preceding dispersion in expectations about the indicator value. Uncertainty, in contrast, increases the response. We rationalize our findings in a model of imperfect information. In the model, dispersion results from a perceived weak link between macroeconomic indicators and fundamentals that reduces the informational content of indicators, while higher fundamental uncertainty makes this informational content more valuable.
Late Payments, Liquidity Constraints and the Mismatch Between Due Dates and Paydays

This paper examines a small random liquidity shock to reveal the effect of liquidity constraints on late payment behavior. In Jerusalem, water bill due dates are randomly determined and therefore may occur just before or after social security paydays. We compared the likelihood of late payments by low‐income households when they receive their social benefits a day after the water bill due date to the likelihood of late payment by the same households when they receive their benefits a day or more before the water bill due date. Using a large administrative data set, we found that a small random liquidity shock leads to a substantial increase in late payments of more than 10 percentage points among income support recipients and around 6 percentage points for old‐age pension recipients with supplementary income. The mismatch between utility payment due dates and paydays may result in interest charges and high late fees, contributing to the poverty penalty.
The UK's Great Demand and Supply Recession

We revisit UK’s poor productivity performance since the Great Recession by means of both a suitable theoretical framework and firm-level prices and quantities data for detailed products allowing us to both measure demand, and its changes over time, and distinguish between quantity total factor productivity (TFP-Q), i.e., the capacity to turn inputs into more physical output (number of shirts, liters of beer), and what we call revenue total factor productivity (TFPR), i.e., productivity calculated using revenue (or value-added) as a measure of output and so the capacity to turn inputs into more revenue. This in turn allows us to measure how changes in TFP-Q, demand and markups ultimately affected revenue TFP, as well as labour productivity, over the Great Recession. Our findings suggest that the poor UK firms’ productivity performance post-recession is due to both a weakening of demand and a decreasing TFP-Q pushing down sales, markups, revenue TFP and labour productivity.
Public Health Insurance and Medical Spending: Evidence from the ACA Medicaid Expansion

This paper investigates the short-run impact of public insurance expansion under the Affordable Care Act on out-of-pocket medical spending (OOP) and risk exposure among low-income, eligible households as well as the incidence of the cost of providing insurance. Using data from the Medical Expenditures Panel Survey (MEPS), I exploit exogenous variation in Medicaid eligibility rules across states, income groups and time. I find that public insurance eligibility reduced mean OOP by 18.2% among targeted households, but it did not causally increase total expenditures among beneficiaries. Rather, Medicaid expansion shifted the burden of payment from eligible households and private insurance (17% reduction) to taxpayers in the form of public insurance (45.7% increase). The efficiency of these public funds can be summarized by a Marginal Value of Public Funds ranging from 0.06 to 0.59 that is highest for households with at least one pre-existing condition.
Effect of Zinc Status on Sexual Maturation in Children with Transfusion Dependent Thalassemia

Background: A child with thalassemia major has a particular growth pattern, which is relatively normal until age of 910 years; after this age a slowing down of growth velocity and a reduced or absent pubertal growth spurt are observed. Zinc is known to affect normal growth and sexual development and an estimation of 20% to 30%of patients with thalassmia are zinc deficient. Our aim was to determine the relationship between zinc status and Tanner stage to evaluate the effect of zinc deficiency on sexual maturation in transfusion dependent thalassemia. Methods: we included 40 children with transfusion dependent β-thalassemia and 40 sex and age matched apparently healthy children as control group. All children were subjected to thorough history taking and full medical examination with body mass index calculation and pubertal stage assessment using Tanner scale, serum zinc and ferritin levels were assayed. Results: there was a significant delay in sexual maturation assessed by Tanner staging in children with thalassemia than controls (p=0.003). Mean age of menarche in cases was 15.1 ± 1.7 years, this was significantly higher than that in controls which was 11.9 ± 1.3 years (p=0.001). Serum zinc in cases was 58.01±10.58 µg/dl, which was significantly lower than serum zinc of controls (68.37 ± 8.67 µg/dl, p = 0.001). Tanner stage was found to positively correlated with BMI and serum zinc (r= 0.54, p= 0.04 and r=0.22, p=0.012 respectively) and negatively correlated with serum ferritin (r=-0.34, p=0.03). Conclusion: Reduced serum zinc and iron overload in children with thalassmic patients was associated with delayed sexual maturation.
ANTIPROLIFERATIVE, ADME AND POTENTIAL IN SILICO G6PDH INHIBITORY ACTIVITY OF NOVEL 2-(1-BENZOFURAN-2-YL)-4-(5-PHENYL-4H-1, 2, 4-TRIAZOL-3-YL) QUINOLINE DERIVATIVES

Objectives: Synthesis of new 2-(1-benzofuran-2-yl)-4-(5-phenyl-4 H -1, 2, 4-triazol-3-yl) quinoline and its derivatives for antiproliferative potential against cancer cells. Methods: The general methods were employed for the synthesis and the structures were confirmed by IR, 1 H - NMR, 13 C - NMR and mass spectral analysis. The antiproliferative activity was performed by 3-(4,5- di methyl thiazol -2-yl)-2,5-di phenyl tetrazolium bromide (MTT) assay and molecular docking study were performed by Auto Dock Tools. In silico Absorption-Distribution-Metabolism-Excretion-Toxicity (ADMET) study for the drug, likeliness was carried out on ACD/lab-2. Results: The compound 3l showed 44, 44, 38 and 37 % inhibition against MCF - 7, HepG2, Colo205 and HeLa cell lines, respectively; whereas, the compounds 3i and 3j exhibited 49 and 42 % inhibition against MCF - 7 cell line. The molecular docking study revealed that the compound 3i has the lowest binding energy ( - 8.60 Kcal mol -1 ), suggesting to be potentially best inhibitor of Glucose - 6 - phosphate dehydrogenase (G6PDH). The in silico ADME analysis also revealed that compound 3i does not violate any of the Lipinski rules of five and has the best stimulative human colonic absorption up to 95 %. Conclusion: The study reveals that the compounds containing benzofuran coupled nitrogen heterocycles are essential for activity as they possess excellent drug - like characteristics.
Working Their Way Up? Us Immigrants&Apos; Changing Labor Market Assimilation in the Age of Mass Migration

Whether immigrants advance in labor markets during their lifetimes relative to natives is a fundamental question in the economics of immigration. We examine linked census records for five cohorts spanning 1850–1940, when immigration to the United States was at its peak. We find a U-shaped pattern of assimilation: immigrants were “catching up” to natives in the early and later cohorts, but not in between. This change was not due to shifts in immigrants’ source countries. Instead, it was rooted in men’s early-career occupations, which we associate with structural change, strengthening complementarities, and large immigration waves in the 1840s and 1900s. (JEL J15, J24, J61, J82, N31, N32)
High-Resolution Particle-Scale Simulation of Biomass Pyrolysis

Pyrolysis is an important process encountered in direct combustion, gasification, and fast pyrolysis of solid biomass materials. This paper describes a computational study of biomass pyrolysis at the particle scale, considering the intraparticle transport phenomena and flow fields surrounding the particles. The lattice Boltzmann method, chosen to solve the conservation equations, was coupled with a chemical reaction mechanism to simulate the pyrolysis kinetics. The flow field was directly resolved at a scale smaller than the particle size, and Darcy’s law was employed to simulate the gas flow in the porous biomass particle. The present numerical method was validated by predicting the pyrolysis process of a single biomass particle. The numerical results agreed with experimental data on the temperature history and extent of conversion of the biomass particle under pyrolysis conditions. A small-scale fast pyrolysis reactor was also simulated. The numerical results indicated that the pyrolysis process may be .
Price Indexes for Microsoft&Apos;S Personal Computer Software Products

Using a unique and comprehensive data source, we measure price changes for Microsoft's desktop personal computer software products during the time period July 1993 through June 2001. This paper contributes to a relatively small literature on price measurement of pre-packaged software by incorporating important channels of distribution, such as volume licensing and Original Equipment Manufacturer, as well as changes in product form, such as upgrades and integrated productivity suites, into matched-model price indexes. Although there are differences over time periods and across products, we find that the prices of Microsoft's desktop operating systems and applications have generally been falling over this time period.
Save the Companion: Why Technological Leaders Share their Research Output with Competitors

This paper investigates the reasons why technological leaders voluntarily share their research outputs with competitors, especially those with lower research capability. Building a two-stage analytical model, we show that technological leaders have incentives to create cooperative knowledge partnership in order to induce competitors to commit more efforts to R&D. With weak knowledge spillovers, firms with low research capability are discouraged from doing R&D because they lack research capability to compete for innovation. Under some conditions, those discouraged competitors harm leading firms' profitability because the leaders produce most knowledge for the industry. Hence, the leading firms have incentives to share their R&D outputs to encourage competitors to contribute more to the knowledge pool in the industry, which is beneficial to both of them. The leader’s incentive increases as unintended knowledge leakage is in low level, the leader’s research capability is moderate and capability gap between firms is small. Interestingly, a high level of capability gap also makes it possible for firms to form knowledge partnership if the lagging competitor is far inefficient. We found supportive empirical evidence for our model. Competing firms are more likely to cooperate in R&D as the level of unintended knowledge spillovers is lower and the asymmetry in their R&D productivities is larger. Our study sheds new light on firm cooperative behavior by providing theoretical foundation for knowledge partnering among asymmetric competitors.
Breakpoint modelling of temporal associations between non-pharmaceutical interventions and the incidence of symptomatic COVID-19 in the Republic of Ireland

Background To constrain propagation and mitigate the burden of COVID-19, most countries initiated and continue to implement several non-pharmaceutical interventions (NPIs), including national and regional lockdowns. In the Republic of Ireland, the first national lockdown was decreed on 23rd of March 2020, followed by a succession of restriction increases and decreases (phases) over the the following year. To date, the effects of these interventions remain unclear, and particularly within differing population subsets. The current study sought to assess the impact of individual NPI phases on COVID-19 transmission patterns within delineated population subgroups in the Republic of Ireland. Methods and Findings Confirmed, anonymised COVID-19 cases occurring between the 29th of February 2020 and 30th November 2020 (n= 72,654) were obtained. Segmented modelling via breakpoint regression with multiple turning points was employed to identify structural breaks across sub-populations, including primary/secondary infections, age deciles, urban/commuter/rural areas, patients with underlying health conditions, and socio-demographic profiles. These were subsequently compared with initiation dates of eight overarching NPI phases. Five distinct breakpoints were identified. The first breakpoint, associated with a decrease in the daily COVID-19 incidence, was reported within 14 days of the first set of restrictions in mid-March 2020 for most population sub-groups. Results suggest that moderately strict NCIs were more effective than the strictest Phase 5 (National Lockdown). Divergences were observed across population sub-groups; lagged response times were observed among populations >80 years, residents of rural/ commuter regions, and cases associated with a below-median deprivation score. Conclusions Study findings suggest that many NPIs have been successful in decreasing COVID-19 incidence rates, however the strictest Phase 5 NPI was not. Moreover, NPIs were not equally successful across all sub-populations, with differing response times noted. Mitigation strategies and interventions may need to be increasingly bespoke, based on sub-population profiles and required responses.
The Intrinsic Scatter of Galaxy Scaling Relations

We present a compendium of disk galaxy scaling relations and a detailed characterization of their intrinsic scatter. Observed scaling relations are typically characterized by their slope, intercept, and scatter; however, these parameters are a mixture of observational errors and astrophysical processes. We introduce a novel Bayesian framework for computing the intrinsic scatter of scaling relations that accounts for nonlinear error propagation and covariant uncertainties. Bayesian intrinsic scatters are ∼25% more accurate than those obtained with a first-order classical method, which systematically underestimates the true intrinsic scatter. Structural galaxy scaling relations based on velocity (V 23.5), size (R 23.5), luminosity (L 23.5), color (g − z), central stellar surface density (Σ1), stellar mass (M *), dynamical mass (M dyn), stellar angular momentum (j *), and dynamical angular momentum (j dyn) are examined to demonstrate the power and importance of the Bayesian formalism. Our analysis is based on a diverse selection of over 1000 late-type galaxies from the Photometry and Rotation Curve Observations from Extragalactic Surveys compilation with deep optical photometry and extended rotation curves. We determine the tightest relation for each parameter by intrinsic orthogonal scatter, finding M * − V 23.5, R 23.5 − j *, and L 23.5 − j dyn to be especially tight. The scatter of the R 23.5 − L 23.5, V 23.5 − (g − z), and R 23.5 − j dyn relations is mostly intrinsic, making them ideal for galaxy formation and evolutionary studies. Our code to compute the Bayesian intrinsic scatter of any scaling relation is also presented. We quantify the correlated nature of many uncertainties in galaxy scaling relations and scrutinize the uncertain nature of disk inclination corrections and their effect on scatter estimates.
Regularized regression on compositional trees with application to MRI analysis

A compositional tree refers to a tree structure on a set of random variables where each random variable is a node and composition occurs at each non‐leaf node of the tree. As a generalization of compositional data, compositional trees handle more complex relationships among random variables and appear in many disciplines, such as brain imaging, genomics and finance. We consider the problem of sparse regression on data that are associated with a compositional tree and propose a transformation‐free tree‐based regularized regression method for component selection. The regularization penalty is designed based on the tree structure and encourages a sparse tree representation. We prove that our proposed estimator for regression coefficients is both consistent and model selection consistent. In the simulation study, our method shows higher accuracy than competing methods under different scenarios. By analysing a brain imaging data set from studies of Alzheimer's disease, our method identifies meaningful associations between memory decline and volume of brain regions that are consistent with current understanding.