Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of Hydrogen Peroxide Biosensor Based on Immobilization of Hemoglobin on Screen-Printed Carbon Electrode Modified With Silver Nanoparticles

The direct electron transfer behavior of hemoglobin that is immobilized onto screen-printed carbon electrode (SPCE) modified with silver nanoparticles (AgNPs) and chitosan (CS) was studied in this work. Cyclic voltametry and spectrophotometry were used to characterize the hemoglobin (Hb) bioconjunction with AgNPs and CS. Results of the modified electrode showed quasi-reversible redox peaks with a formal potential of (-0.245V) versus Ag/AgCl in 0.1M phosphate buffer solution (PBS), pH7, at a scan rate of 0.1Vs-1. The charge transfer coefficient (Î±) was 0.48 and the apparent electron transfer rate constant (Ks) was 0.47s-1. The electrode was used as a hydrogen peroxide biosensor with a linear response over 3 to 240 ÂµM and a detection limit of 0.6 ÂµM. As a result, the modified biosensor here has exhibited a high sensitivity, good reproducibility and stability.
Paper—Blended Learning Integrated Fieldwork on Fundamentals of Geology Learning During the Covid Blended Learning Integrated Fieldwork on Fundamentals of Geology Learning During the Covid-19 Pandemic

The purpose of this research was to test the blended learning integrated fieldwork in the Fundamentals of Geology learning. Classroom Action Research (CAR) was used in this study. The research subjects consisted of 32 Grade 10 Mining Geology students of SMK Negeri 1 Binuang in the 2019/2020 academic year. Data analysis was carried out using quantitative descriptive analysis, namely by observing the research data which was then followed by synthesis using a literature review. From the results of research during the Pre-Cycle, it was known that the number of students who met the Minimum Completeness Criteria (KKM) was 18.75%, while in Cycle I it is 84.374%, and Cycle II was 77.78%. These results indicated that there was an increase from Pre-Cycle to Cycle I, but there was a decrease in Cycle I to Cycle II. The results obtained in this CAR showed that blended learning integrated fieldwork has the potential to be effective as a learning model for the Fundamentals of Geology during the COVID-19 Pandemic. Some things that need to be considered so that blended learning integrated fieldwork can be effective in learning is by paying attention to scientific steps during learning, choosing fieldwork locations that are accessible to accessibility and there are communication and internet signals, as well as intensive guidance from the teacher. Regarding the COVID-19 Pandemic, it is also necessary to design integrated blended learning fieldwork that complies with health protocols. Keywords—Fieldwork, Blended learning, Fundamentals of Geology, COVID-
Effects of Digital Badges on the Online Activity of University Students in a Blended Learning Model

This research aimed to determine the effects a digital badge program had on the academic performance and participation in online activities in the English I course. The research design was experimental with post-test and control group. The sample consisted of 18 students from the first semester, enrolled in a blended learning model in a private university in Lima. There were statistically significant differences between the control and experimental group, which favored the experimental group. In addition, an effect size of 4.56 was found, which was considered very large. Regarding academic performance, no statistically significant differences were found. However, the effect size was 0.60, considered moderate. In conclusion, the digital badge program encouraged participation in virtual academic activities; however, the results obtained in connection with academic performance are not entirely clear, which is why it is suggested to conduct research in courses of different areas, with a broader sample.
Consolidated Advantage: New Organizational Dynamics of Wage Inequality

The two main axes of inequality in the U.S. labor market—occupation and workplace—have increasingly consolidated. In 1999, the largest share of employment at high-paying workplaces was blue-collar production workers, but by 2017 it was managers and professionals. As such, workers benefiting from a high-paying workplace are increasingly those who already benefit from membership in a high-paying occupation. Drawing on occupation-by-workplace data, we show that up to two-thirds of the rise in wage inequality since 1999 can be accounted for not by occupation or workplace inequality alone, but by this increased consolidation. Consolidation is not primarily due to outsourcing or to occupations shifting across a fixed set of workplaces. Instead, consolidation has resulted from new bases of workplace pay premiums. Workplace premiums associated with teams of professionals have increased, while premiums for previously high-paid blue-collar workers have been cut. Yet the largest source of consolidation is bifurcation in the social sector, whereby some previously low-paying but high-professional share workplaces, like hospitals and schools, have deskilled their jobs, while others have raised pay. Broadly, the results demonstrate an understudied way that organizations affect wage inequality: not by directly increasing variability in workplace or occupation premiums, but by consolidating these two sources of inequality.
SARS-CoV-2 epidemic in the South American Southern cone: can combined immunity from vaccination and infection prevent the spread of Gamma and Lambda variants while easing restrictions?

All South American countries from the Southern cone (Argentina, Brazil, Chile, Paraguay and Uruguay) experienced severe COVID-19 epidemic waves during early 2021 driven by the expansion of variants Gamma and Lambda, however, there was an improvement in different epidemic indicators since June 2021. To investigate the impact of national vaccination programs and natural infection on viral transmission in those South American countries, we analyzed the coupling between population mobility and the viral effective reproduction number Rt. Our analyses reveal that population mobility was highly correlated with viral Rt from January to May 2021 in all countries analyzed; but a clear decoupling occurred since May-June 2021, when the rate of viral spread started to be lower than expected from the levels of social interactions. These findings support that populations from the South American Southern cone probably achieved the conditional herd immunity threshold to contain the spread of regional SARS-CoV-2 variants.
Finding optimal ways to express macroeconomic views in US Treasury Rates: A framework approach

This paper aims to provide a trading framework approach with an evidence-based set of trading signals and risk-reward metrics that identify the optimal way to express a macroeconomic view in the U.S Treasury market. The paper develops the trading framework which captures the two principal investment approaches: the fundamental and the quantitative. The framework consists of the two driving factors of the term structure of interest: level and slope. Based on the available treasury instruments for each driving factor, the paper develops the trading framework. The fundamental framework consists of a fair value model and a monetary policy model: the fair value model with an average hit rate of 54 percent allows the identification of which instrument has cheapened or richened beyond what is implied by market variables with the rest of the curve. The monetary policy model with an average of 80 hit rate identifies the trading bias suggested by the current Federal Open Market Committee (FOMC) tone of language and the monetary cycle regime. On the other hand, the quantitative model consists of three models which capture the following three technical factors in Treasury rates: The momentum model with an average hit rate of 68 allows the identification of current and potential trends. The seasonality model with an average hit rate of 53 allows identifying the instrument's trading bias based on its historical performance in the same given time frame. Finally, the volatility model enables the identification of the best risk/reward trade given the current volatility regime.
On the Pathways of N-Doped Carbon Materials Active Sites During Oxygen Reduction Reaction

N-doped carbon materials have been considered as one of the most promising options for the replacement of platinum-based electrocatalysts towards the oxygen reduction reaction. However, what is known about this kind of catalysts and the identification of the active sites is still contradictory. Most of the scientific literature focuses on experimental characterization before ORR testing, leading to a consistent lack of knowledge about the surface chemistry at operando conditions. The development of operando techniques is, up to now, not enough to unravel with accuracy the reasons why these catalysts are as active as platinum-based electrodes and to understand the deactivation with time of use. In this work, the changes occurring in the active sites of N-doped carbon catalysts have been analysed in detail through pre- and post-ORR extended characterization of selectively N-doped carbon materials, along with sophisticated computational modelling.
The Pandemic's Effect on Demand for Public Schools, Homeschooling, and Private Schools

The Covid-19 pandemic drastically disrupted the functioning of U.S. public schools, potentially changing the relative appeal of alternatives such as homeschooling and private schools. Using longitudinal student-level administrative data from Michigan and nationally representative data from the Census Household Pulse Survey, we show how the pandemic affected families’ choices of school sector. We document four central facts. First, public school enrollment declined noticeably in fall 2020, with about 3 percent of Michigan students and 10 percent of kindergartners using other options. Second, most of this was driven by homeschooling rates jumping substantially, driven largely by families with children in elementary school. Third, homeschooling increased more where schools provided in-person instruction while private schooling increased more where instruction was remote, suggesting heterogeneity in parental concerns about children’s physical health and instructional quality. Fourth, kindergarten declines were highest among low income and Black families while declines in other grades were highest among higher income and White families, highlighting important heterogeneity by students’ existing attachment to public schools. Our results shed light on how families make schooling decisions and imply potential longer-run disruptions to public schools in the form of decreased enrollment and funding, changed composition of the student body, and increased size of the next kindergarten cohort.
Production Network and GDP Growth: A Decomposition Approach

We propose a novel decomposition of the input-output linkage impact into linkage efficiency and linkage strength. We show that, for standard multisectoral models, countries' GDP growth rates are related to the average total backward linkages in the economy, which we call linkage strength, and to the covariance between sectoral backward linkages and the sectoral shares in final demand, which we call linkage efficiency. Using the World Input-Output Database, we further demonstrate that what matters for GDP growth is the linkage efficiency.
Natural Resources and Economic Growth: The Role of Financial Development Evidence from MENA Region

It is often postulated that counties with natural resources endowment could experience a significant increase in economic growth. However, empirical evidence has revealed that this is not the case for many resource-rich countries. The Middle East and North Africa region has the world’s largest natural resource endowments, yet there is a lack of empirical evidence of this resource abundance on economic growth in the region. More so, it is expected that the level of financial development could expunge the negative effect of resource abundance on economic growth. However, most existing studies disregard this important role of financial development in estimating the impact resource abundance on economic growth. To this end, this study investigated the long-run impact of resource abundance on economic growth and accounted for the role of financial development on the natural resource–growth nexus. To achieve this, the fully modified ordinary least squares and dynamic ordinary least squares were employed for a sample of 11 MENA countries over the period 1987-2015. The results revealed that the abundance of natural resources could generate long-run economic growth in selected MENA countries. However, the impact of growth is even more pronounced in countries with a relatively lower (fair level of) financial development.
Persistent Monetary Policy in a Model with Labor Market Frictions

In a basic New Keynesian DSGE model with involuntary unemployment and inflation target shocks, we study the role of labor markets in the transmission of persistent monetary policy shocks that increase households' inflation expectations. The model predicts that labor market conditions can play important role in the transmission channel of the persistent inflation target shock: quantitatively realistic labor market frictions increase the expansionary effect of inflation target shock on output by around a half compared to that under the model without labor market frictions. Using a VAR analysis, we further provide empirical evidence consistent with the predictions of our theoretical model.
Kuramoto synchronization of quantum tunneling polarons for describing the dynamic structure in cuprate superconductors

A major open topic in cuprates is the interplay between the lattice and electronic dynamics and the importance of their coupling to the mechanism of high-temperature superconductivity (HTSC). As evidenced by Extended X-ray Absorption Fine Structure experiments (EXAFS), anharmonic structural effects are correlated with the charge dynamics and the transition to a superconducting phase in different HTSC compounds. Here we describe how structural anharmonic effects can be coupled to electronic and lattice dynamics in cuprate systems by performing the exact diagonalization of a prototype anharmonic many-body Hamiltonian on a relevant six-atom cluster and show that the EXAFS results can be understood as a Kuramoto synchronization between coupled internal quantum tunneling polarons associated with the two-site distribution of the copper-apical-oxygen ($Cu-O_{ap}$) pair in the dynamic structure. Furthermore, we find that this first order, anti-phase synchronization transition can be fine tuned by temperature and anharmonicity of the lattice vibrations, and promotes the pumping of charge, initially stored at the apical oxygen reservoirs, into the copper-oxide planes. Simultaneously, the internal quantum tunneling polaron extends to the copper-planar-oxygen ($Cu-O_{pl}$) pair. All these findings support an interpretation of the EXAFS data in terms of an effective, quantum mechanical triple-well-potential, which accurately represents the anti-phase synchronization of apical oxygens displacements and lattice-assisted charge transfer to the $CuO_2$ plane.
DisCoDisCo at the DISRPT2021 Shared Task: A System for Discourse Segmentation, Classification, and Connective Detection

This paper describes our submission to the DISRPT2021 Shared Task on Discourse Unit Segmentation, Connective Detection, and Relation Classification. Our system, called DisCoDisCo, is a Transformer-based neural classifier which enhances contextualized word embeddings (CWEs) with hand-crafted features, relying on tokenwise sequence tagging for discourse segmentation and connective detection, and a feature-rich, encoder-less sentence pair classifier for relation classification. Our results for the first two tasks outperform SOTA scores from the previous 2019 shared task, and results on relation classification suggest strong performance on the new 2021 benchmark. Ablation tests show that including features beyond CWEs are helpful for both tasks, and a partial evaluation of multiple pretrained Transformer-based language models indicates that models pre-trained on the Next Sentence Prediction (NSP) task are optimal for relation classification.
Modeling and Analysis of Discrete Response Data: Applications to Public Opinion on Marijuana Legalization in the United States

This chapter presents an overview of a specific form of limited dependent variable models, namely discrete choice models, where the dependent (response or outcome) variable takes values which are discrete, inherently ordered, and characterized by an underlying continuous latent variable. Within this setting, the dependent variable may take only two discrete values (such as 0 and 1) giving rise to binary models (e.g., probit and logit models) or more than two values (say $j=1,2, \ldots, J$, where $J$ is some integer, typically small) giving rise to ordinal models (e.g., ordinal probit and ordinal logit models). In these models, the primary goal is to model the probability of responses/outcomes conditional on the covariates. We connect the outcomes of a discrete choice model to the random utility framework in economics, discuss estimation techniques, present the calculation of covariate effects and measures to assess model fitting. Some recent advances in discrete data modeling are also discussed. Following the theoretical review, we utilize the binary and ordinal models to analyze public opinion on marijuana legalization and the extent of legalization -- a socially relevant but controversial topic in the United States. We obtain several interesting results including that past use of marijuana, belief about legalization and political partisanship are important factors that shape the public opinion.
Phase diagram of quantum generalized Potts-Hopfield neural networks

We introduce and analyze an open quantum generalization of the q-state Potts-Hopfield neural network (NN), which is an associative memory model based on multi-level classical spins. The dynamics of this many-body system is formulated in terms of a Markovian master equation of Lindblad type, which allows to incorporate both probabilistic classical and coherent quantum processes on an equal footing. By employing a mean field description we investigate how classical fluctuations due to temperature and quantum fluctuations effectuated by coherent spin rotations affect the ability of the network to retrieve stored memory patterns. We construct the corresponding phase diagram, which in the low temperature regime displays pattern retrieval in analogy to the classical Potts-Hopfield NN. When increasing quantum fluctuations, however, a limit cycle phase emerges, which has no classical counterpart. This shows that quantum effects can qualitatively alter the structure of the stationary state manifold with respect to the classical model, and potentially allow one to encode and retrieve novel types of patterns.
Spin Alignment of Dark Matter Halos: Fast Halos

We investigate the spin alignment of dark matter halos by considering a mechanism somewhat similar to tidal locking; we dub it tidal-locking theory (TLT). While tidal torque theory (TTT) is responsible for the initial angular momentum of dark matter halos, TLT explains the angular momentum evolution during nonlinear ages. Our previous work showed that close encounters between halos could drastically change their angular momentum. This paper argues that TLT predicts partial alignment between the speed and spin direction for large high-speed halos. To examine this prediction, we use IllustrisTNG simulations and look for the alignment of the halos’ rotation axes. We find that the excess probability of alignment between spin and speed is about 10% at z = 0 for the large fast halos with velocities larger than twice the median. This spin–speed alignment weakens at z = 1 and disappears at z = 4. We also show that TTT predicts that the spin of a halo tends to be aligned with the middle eigendirection of the tidal tensor. Moreover, we find that the halos at z = 10 are preferentially aligned with the middle eigendirection of the tidal tensor with an excess probability of 15%. We show that TTT fails to predict the correct alignment at z = 0, while it works almost flawlessly at z = 10. These findings confirm that at earlier redshifts, during which mergers and fly-bys are rare, TTT works well, but after enough time, when fly-bys have occurred, the spin of the halos tends to align with speed for high-speed halos, due to the TLT effect.
Unequal-mass mergers of dark matter haloes with rare and frequent self-interactions

Dark matter self-interactions have been proposed to solve problems on small length scales within the standard cold dark matter cosmology. Here we investigate the effects of dark matter self-interactions in merging systems of galaxies and galaxy clusters with equal and unequal mass ratios. We perform N-body dark matter-only simulations of idealised setups to study the effects of dark matter self-interactions that are elastic and velocity-independent. We go beyond the commonly adopted assumption of large-angle (rare) dark matter scatterings, paying attention to the impact of small-angle (frequent) scatterings on astrophysical observables and related quantities. Specifically, we focus on dark matter-galaxy offsets, galaxy-galaxy distances, halo shapes, morphology and the phase-space distribution. Moreover, we compare two methods to identify peaks: one based on the gravitational potential and one based on isodensity contours. We find that the results are sensitive to the peak finding method, which poses a challenge for the analysis of merging systems in simulations and observations, especially for minor mergers. Large dark matter-galaxy offsets can occur in minor mergers, especially with frequent self-interactions. The subhalo tends to dissolve quickly for these cases. While clusters in late merger phases lead to potentially large differences between rare and frequent scatterings, we believe that these differences are non-trivial to extract from observations. We therefore study the galaxy/star populations which remain distinct even after the dark matter haloes have coalesced. We find that these collisionless tracers behave differently for rare and frequent scatterings, potentially giving a handle to learn about the micro-physics of dark matter.
Similarity of competing risks models with constant intensities in an application to clinical healthcare pathways involving prostate cancer surgery

The recent availability of routine medical data, especially in a university‐clinical context, may enable the discovery of typical healthcare pathways, that is, typical temporal sequences of clinical interventions or hospital readmissions. However, such pathways are heterogeneous in a large provider such as a university hospital, and it is important to identify similar care pathways that can still be considered typical pathways. We understand the pathway as a temporal process with possible transitions from a single initial treatment state to hospital readmission of different types, which constitutes a competing risks setting. In this article, we propose a multi‐state model‐based approach to uncover pathway similarity between two groups of individuals. We describe a new bootstrap procedure for testing the similarity of constant transition intensities from two competing risk models. In a large simulation study, we investigate the performance of our similarity approach with respect to different sample sizes and different similarity thresholds. The studies are motivated by an application from urological clinical routine and we show how the results can be transferred to the application example.