Miracle-dz/UniFAR-FTUs huggingface dataset:
Risk And Benefit Perception Of Electronic Cigarette Among Non-Smokers In Yogyakarta Province

Smoking cigarette or tobacco in any form can be a serious killer and harmful for millions of people every year. Tobacco has been developed into many variants, the latest one is electric cigarette (e-cigarette) or vape. This research aimed to find out the perception of e-cigarette’s risk and benefit among non-smokers and what factors affecting the risk and benefit perception of e-cigarette. This study was a cross-sectional design and used validated self-administered questionnaire. Questionnaire divided into three section: sociodemographic characteristics, smoking characteristics, risk and benefit perception statements. Respondents were non-smokers in the province of Yogyakarta and selected by convenience sampling. Data were analyzed descriptively and bivariate by using Chi-Square to identify the factors associated with risk and benefit perception among non-smokers. The results showed that from 241 respondents, 18 (7.5%) were former smokers who had experience using vape but already quit, and 223 (92.5%) were never use vape. Most of non-smokers respondent in Yogyakarta province have a high perception about e-cigarette risk and low perception about e-cigarette benefit. Respondent sociodemographics (sex, age, education level, current usage of vape and income/pocket money) are not significantly influence the e-cigarette risk perception among non-smokers in Yogyakarta province. Only smoking status or current respondent usage of vape that significantly influence e-cigarette benefit perception among non-smokers in Yogyakarta province (p-value < 0.05). Smoking status or current vape usage from respondent is significantly influence the benefit perception of e-cigarette by OR value 0.155 (0.054-0.444) and p-value 0.001 (p-value <0.05). This study provides novel information on the use and risk-benefit perception of e-cigarette among non-smokers in Yogyakarta. However, further research is needed with different study designs so that it can identify predictors of future e-cigarette use from non-smokers. It is needed to explore the factors associated with e-cigarette use in various regions and cities in Indonesia so it can represent nationally. Further, the government can make a more comprehensive ecigarette policy regarding to the all levels of society.
Climate of an oceanic island in the Eastern Pacific: Isla del Coco, Costa Rica, Central America

Studies of atmosphere-ocean interaction in the Pacific of Costa Rican are scarce. To identify oceanographic systems that may be influencing climate near Cocos Island (Eastern Tropical Pacific Seascape) we conducted six scientific expeditions between 2007 and 2012. Two automated weather stations were set near Chatham and Wafer bays during the expeditions. Data included records from National Meteorological Institute, Global Precipitation Climatology Project (GPCP) and Extended Reconstructed Sea Surface Temperature (ERSST). The climate is typical of the Eastern Tropical Pacific. Its seasonality is driven by precipitation variability associated with meridional migration of the Intertropical Convergence Zone. The seasonal cycle has two peaks, in May and July, a relative minimum between them in June, and the absolute minimum in February. Most of the precipitation is recorded from April to November. Most rain events have short duration and low intensity. An SST trend was observed from January 1854 to December 2013, coherent with regional warming temperature observations. From 1998 to 2013 there were changes in distributions of almost all meteorological parameters. The combination of these factors resulted in higher evapotranspiration values through the daily cycle, especially during the night time. Precipitation (P) positive anomalies tended to be associated with positive air surface temperature (AST) and SST anomalies and negative global radiation (GR) anomalies. Negative P anomalies tended to be associated with negative AST, SST and positive GR anomalies. Relative humidity (RH) negative anomalies tend to be associated with positive wind speed (WS) anomalies, and the WS effect is opposite for positive RH anomalies. During the cold Nino 3 condition of October 2007, negative P, AST, SST and RH anomalies were observed in concordance with positive WS and GR anomalies, in agreement with the conceptual model of climate system response at Isla del Coco to cold ENSO conditions. Rev. Biol. Trop. 64 (Suppl. 1): S59-S74. Epub 2016 February 01.
Prediction of Standard Lactation Curves for Primiparous Holstein Cows by Using Corrected Regression Models

Prediction of the expected milk yield is important for the management of the primiparous cows (PPC) with a few or no data on their own milk productivity. We developed a system of regression equations for predicting milk yields in standard lactation. The models include the systematic effects of the calving season, the five-year rolling herd average of milk yield of PPC, the breeding values of the parents for milk production, and daily milk recordings. A total of 21,901 lactations of Holstein PPC were collected during the regular monthly milk recordings of cows in the Republic of Slovenia. By including daily milk recordings in the model, the coefficients of determination of regression models for the prediction of milk yield increase: without known recordings (M0) R2=0.80; with one recording (M1) R2=0.82; with two first consecutive recordings (M2) R2=0.86; and with three recordings (M3) R2=0.89. Deviations of milk yield up to 500 kg in a standard lactation (<1.6 kg/day) were as follows: with the model M0, they occurred in 53.4% of PPC; with M1, they occurred in 56.3% of PPC; with M2, they occurred in 64.5% of PPC; and with M3, they occurred in 70.9% of PPC. We concluded that the developed system of regression models is an appropriate method for milk yield prediction of PPC.
The impact of group membership on punishment versus partner choice

Human punishment is biased by ingroup favoritism: we tend to punish outgroup members more harshly than ingroup members. Such preferential punishment of outgroup members could reflect an influence of group status specifically on punishment, or may reflect a general tendency to respond to outgroup members’ behavior more harshly, regardless of the type of response used. To investigate this question, we contrasted punishment with the decision to reject a partner, often termed partner choice. In two studies, participants interacted with other players in an incentivized economic game. We assigned participants to groups using a “minimal” groups paradigm (Study 1) or a consequential political position (Study 2). Across both studies, when participants could respond to their partner’s behavior with punishment, they punished outgroup members more harshly than ingroup members, replicating past work. We also extend prior work by showing that this difference principally reflects outgroup derogation rather than ingroup love, through the inclusion of neutral individuals in Study 2. In contrast, when participants could respond by either continuing to interact with their current partner or instead be paired with a new player, participants’ decisions were almost completely unaffected by group membership. Thus, group membership has a strong influence on how we punish others, but almost no influence on how we make partner choice decisions. These results shed light on the breadth of influence group membership can have, especially on how we respond to transgressions, and provide insight into the unique psychological processes supporting punishment and partner choice decisions. Impact of group membership 3
Measurement report: Cloud Processes and the Transport of Biological Emissions Regulate Southern Ocean Particle and Cloud Condensation Nuclei Concentrations

Abstract. Long-range transport of biogenic emissions from the coast of Antarctica, precipitation scavenging, and cloud processing are the main processes that influence the observed variability in Southern Ocean (SO) marine boundary layer (MBL) condensation nuclei (CN) and cloud condensation nuclei (CCN) concentrations during the austral summer. Airborne particle measurements on the HIAPER GV from north-south transects between Hobart, Tasmania and 62° S during the Southern Ocean Clouds, Radiation Aerosol Transport Experimental Study (SOCRATES) were separated into four regimes comprising combinations of high and low concentrations of CCN and CN. In 5-day HYSPLIT back trajectories, air parcels with elevated CCN concentrations were almost always shown to have crossed the Antarctic coast, a location with elevated phytoplankton emissions relative to the rest of the SO. The presence of high CCN concentrations was also consistent with high cloud fractions over their trajectory, suggesting there was substantial growth of biogenically formed particles through cloud processing. Cases with low cloud fraction, due to the presence of cumulus clouds, had high CN concentrations, consistent with previously reported new particle formation in cumulus outflow regions. Measurements associated with elevated precipitation during the previous 1.5-days of their trajectory had low CCN concentrations indicating CCN were effectively scavenged by precipitation. A course-mode fitting algorithm was used to determine the primary marine aerosol (PMA) contribution which accounted for 0.07 µm) indicated that particle formation occurs more frequently above the MBL; however, the growth of recently formed particles typically occurs in the MBL, consistent with cloud processing and the condensation of volatile compound oxidation products. CCN measurements on the R/V Investigator as part of the second Clouds, Aerosols, Precipitation, Radiation and atmospheric Composition Over the southeRn Ocean (CAPRICORN-2) campaign were also conducted during the same period as the SOCRATES study. The R/V Investigator observed elevated CCN concentrations near Australia, likely due to continental and coastal biogenic emissions. The Antarctic coastal source of CCN from the south as well as CCN sources from the mid-latitudes create a latitudinal gradient in CCN concentration with an observed minimum in the SO between 55° S and 60° S. The SOCRATES airborne measurements are not influenced by Australian continental emissions, but still show evidence of elevated CCN concentrations to the south of 60° S, consistent with biogenic coastal emissions. In addition, a latitudinal gradient in the particle composition is observed; more hygroscopic particles to the north, consistent with a greater fraction of sea salt from PMA, and more sulfate and organic particles to the south, which are likely from biogenic sources in coastal Antarctica.
The revolution will be hard to evaluate: How simultaneous change in multiple policies affects policy-based health research

Extensive empirical health research leverages variation in the timing and location of policy changes as quasi-experiments. Multiple social policies may be adopted simultaneously in the same locations, creating clustering which must be addressed analytically for valid inferences. The pervasiveness and consequences of policy clustering have received limited attention. We analyzed a systematic sample of 13 social policy databases covering diverse domains including poverty, paid family leave, and tobacco. We quantified policy clustering in each database as the fraction of variation in each policy measure across jurisdictions and times that could be explained by co-variation with other policies (R2). We used simulations to estimate the ratio of the variance of effect estimates under the observed policy clustering to variance if policies were independent. Policy clustering ranged from very high for state-level cannabis policies to low for country-level sexual minority rights policies. For 65% of policies, greater than 90% of the place-time variation was explained by other policies. Policy clustering increased the variance of effect estimates by a median of 57-fold. Policy clustering poses a major methodological challenge to rigorously evaluating health effects of individual social policies. Tools to enhance validity and precision for evaluating clustered policies are needed.
Testing mobile air purifiers in a school classroom: Reducing the airborne transmission risk for SARS-CoV-2

Abstract Airborne transmission of SARS-CoV-2 through virus-containing aerosol particles has been established as an important pathway for Covid-19 infection. Suitable measures to prevent such infections are imperative, especially in situations when a high number of persons convene in closed rooms. Here we tested the efficiency and practicability of operating four air purifiers equipped with HEPA filters in a high school classroom while regular classes were taking place. We monitored the aerosol number concentration for particles >3 nm at two locations in the room, the aerosol size distribution in the range from 10 nm to 10 µm, PM10 and CO2 concentration. For comparison, we performed similar measurements in a neighboring classroom without purifiers. In times when classes were conducted with windows and door closed, the aerosol concentration was reduced by more than 90% within less than 30 min when running the purifiers (air exchange rate 5.5 h−1). The reduction was homogeneous throughout the room and for all particle sizes. The measurements are supplemented by a calculation estimating the maximum concentration levels of virus-containing aerosol from a highly contagious person speaking in a closed room with and without air purifiers. Measurements and calculation demonstrate that air purifiers potentially represent a well-suited measure to reduce the risks of airborne transmission of SARS-CoV-2 substantially. Staying for 2 h in a closed room with a highly infective person, we estimate that the inhaled dose is reduced by a factor of six when using air purifiers with a total air exchange rate of 5.7 h−1.
Effect of water intake and storage time on protein concentration and enzyme AChE activity in erythrocyte and plasma blood samples of healthy individuals

Background: Storage time influence concentration levels of blood biomarker. This study aimed to assess the effect of water intake prior sampling and storage time on protein concentration, enzyme AChE activity, inhibitor efficacy and to build an efficient inhibitor calibration curve in healthy individuals. Methods: Data analysis was performed on 11 participants. Study utilizes substrate acetylcholine chloride and inhibitors BW284c51 (0.01mM) and GUK-987 (0.1mM). Calibration curve ranging from 10-1 to 10-38 mM was build for inhibitor GUK-987 and GDK-510. Data analysis is carried out with Microsoft Excel 2007. Data analysis was performed via IBM SPSS Statistical Software v23.0. Descriptive statistics and parametric statistical tests were assessed for 0, 90, 91 and 92 days of storage in Plasma and Erythrocyte samples. Workflow of building calibration curve and the most efficient inhibition concentration is assessed. Results: Water intake and storage time have effect on in vitro protein concentration, activation/inhibition of enzyme AChE activity in Plasma and Erythrocyte samples. However, 100% inhibitor efficacy is maintained for inhibitor GUK-987 in Plasma samples and inhibitor BW284c51 in Erythrocyte samples. The most efficient inhibitor concentration is determined. Conclution: Significant changes and variable association have been estimated between protein concentration, activation/inhibition of enzyme AChE activity, as a cause of water intake and storage time. Taking all these factors into account for further research is important for disease prevention and human wellbeing. Keywords: Water intake, Storage time, Enzyme AChE, Erythrocyte samples, Plasma sample, Healthy individuals
RODE: Learning Roles to Decompose Multi-Agent Tasks

Role-based learning holds the promise of achieving scalable multi-agent learning by decomposing complex tasks using roles. However, it is largely unclear how to efficiently discover such a set of roles. To solve this problem, we propose to first decompose joint action spaces into restricted role action spaces by clustering actions according to their effects on the environment and other agents. Learning a role selector based on action effects makes role discovery much easier because it forms a bi-level learning hierarchy -- the role selector searches in a smaller role space and at a lower temporal resolution, while role policies learn in significantly reduced primitive action-observation spaces. We further integrate information about action effects into the role policies to boost learning efficiency and policy generalization. By virtue of these advances, our method (1) outperforms the current state-of-the-art MARL algorithms on 10 of the 14 scenarios that comprise the challenging StarCraft II micromanagement benchmark and (2) achieves rapid transfer to new environments with three times the number of agents. Demonstrative videos are available at this https URL .
Chemical diversity among A–B stars with low rotational velocities: non-LTE abundance analysis

We present accurate element abundance patterns based on the non-local thermodynamic equilibrium (non-LTE, NLTE) line formation for 14 chemical elements from He to Nd for a sample of nine A9 to B3 type stars with well determined atmospheric parameters and low rotational velocities. We constructed new model atom of Zr II-III and updated model atoms for Sr II and Ba II by implementing the photoionization cross sections from calculations with the Dirac B-spline R-matrix method. The NLTE abundances of He to Fe in the stars HD~17081, HD~32115, HD~160762, and HD~209459 are found to be consistent with the solar abundances, and HD~73666 being a Blue Struggler does not reveal deviations from chemical composition of the Praesepe cluster. Three of these stars with an effective temperature of lower than 10500~K have supersolar abundances of Sr, Zr, Ba, and Nd, and our results suggest the presence of a positive correlation between stellar effective temperature and abundance. For each star, enhancement of Ba is higher than that for any other heavy element. We propose that the solar Ba abundance is not representative of the galactic Ba abundance at modern epoch. The status of HD~145788 was not clarified: this star has solar abundances of C to Si and enhancements of Sr to Ba similar to that for superficially normal stars of similar temperature, while overabundant Ca, Ti, and Fe. The NLTE abundances of Vega support its status of a mild lambda~Bootis star.
General teleparallel modifications of Schwarzschild geometry

Teleparallel theories of gravity are described in terms of the tetrad of a metric and a flat connection with torsion. In this paper, we study spherical symmetry in a modified teleparallel theory of gravity which is based on an arbitrary function of the five possible scalars constructed from the irreducible parts of torsion. This theory is a generalisation of the so-called New General Relativity theory. We find that only two scalars are different to zero in spherical symmetry and we solve the corresponding field equations analytically for conformal Teleparallel gravity, and then perturbatively around Schwarzschild geometry for the general perturbative theory around GR. Finally we compute phenomenological effects from the perturbed solutions such as the photon sphere, perihelion shift, Shapiro delay and the light deflection. We find their correspondent correction to the standard GR contribution and their dependence on the three model parameters.
High Aptitude Motor-Imagery BCI Users Have Better Visuospatial Memory

Brain-computer interfaces (BCI) decode the electrophysiological signals from the brain into an action that is carried out by a computer or robotic device. Motor-imagery BCIs (MI-BCI) rely on the user’s imagination of bodily movements, however not all users can generate the brain activity needed to control MI-BCI. This difference in MI-BCI performance among novice users could be due to their cognitive abilities. In this study, the impact of spatial abilities and visuospatial memory on MI-BCI performance is investigated. Fifty-four novice users participated in a MI-BCI task and two cognitive tests. The impact of spatial abilities and visuospatial memory on BCI task error rate in three feedback sessions was measured. Our results showed that spatial abilities, as assessed by the Mental Rotation Test, were not related to MI-BCI performance, however visuospatial memory, assessed by the design organization test, was higher in high aptitude users. Our findings can contribute to optimization of MI-BCI training paradigms through participant screening and cognitive skill training.
Weighted Sum-Rate Maximization for Multi-IRS-Assisted Full-Duplex Systems With Hardware Impairments

Smart and reconfigurable wireless communication environments can be established by exploiting well-designed intelligent reflecting surfaces (IRSs) to shape the communication channels. In this paper, we investigate how multiple IRSs affect the performance of multi-user full-duplex communication systems under hardware impairment at each node, wherein the base station (BS) and the uplink users are subject to maximum transmission power constraints. Firstly, the uplink-downlink system weighted sum-rate (SWSR) is derived as a system performance metric. Then, we formulate the resource allocation design to maximize the SWSR as an optimization problem which jointly optimizes the beamforming and the combining vectors at the BS, the transmit powers of the uplink users, and the phase shifts of multiple IRSs. Since the SWSR optimization problem is non-convex, an efficient iterative alternating approach is proposed to obtain a suboptimal solution for the design problem. In particular, we first reformulate the main problem into an equivalent weighted minimum mean-square-error form and then transform it into several convex sub-problems which can be analytically solved for given phase shifts. Then, the IRSs phases are optimized via a gradient ascent-based algorithm. Finally, numerical results are presented to clarify how multiple IRSs enhance the performance metric under hardware impairment.
Putting Theory to Work: From Learning Bounds to Meta-Learning Algorithms

Most of existing deep learning models rely on excessive amounts of labeled training data in order to achieve state-of-the-art results, even though these data can be hard or costly to get in practice. One attractive alternative is to learn with little supervision, commonly referred to as few-shot learning (FSL), and, in particular, meta-learning that learns to learn with few data from related tasks. Despite the practical success of meta-learning, many of its algorithmic solutions proposed in the literature are based on sound intuitions, but lack a solid theoretical analysis of the expected performance on the test task. In this paper, we review the recent advances in meta-learning theory and show how they can be used in practice both to better understand the behavior of popular meta-learning algorithms and to improve their generalization capacity. This latter is achieved by integrating the theoretical assumptions ensuring efficient meta-learning in the form of regularization terms into several popular meta-learning algorithms for which we provide a large study of their behavior on classic few-shot classification benchmarks. To the best of our knowledge, this is the first contribution that puts the most recent learning bounds of meta-learning theory into practice for the popular task of few-shot classification.