Miracle-dz/UniFAR-FTUs huggingface dataset:
618 Qualitative analysis of bedtime challenges and parental interventions in foster care children

Sleep during childhood has a major impact on physiological, psychological, and cognitive development. Limited research has focused on vulnerable populations such as children in foster care. Foster care children endure placement instability, which may contribute to disrupted sleep patterns and unpredictability around bedtime (Leathers, et al., 2019). The Fostering Sleep study examined foster caregivers’ perceptions of children’s sleep challenges and strategies for improving sleep difficulties. Foster caregivers of children ages 4–11 throughout the US were invited to complete the Qualtrics Sleep Health among Children in Foster Care Survey via foster care Facebook groups. The survey included quantitative and qualitative questions focused on sleep patterns and behaviors. Qualitative questions on barriers and opportunities to improve sleep were examined: what helps foster care children sleep well; what kinds of difficulties do foster care children experience at bedtime? 483 foster care parents responded. Responses to bedtime difficulties were coded using 27 categories (e.g., parasomnias, bedtime resistance, trouble self-soothing); responses to what helps your child sleep well were coded based on 22 categories (e.g., comfort items, melatonin, TV as in intervention, communication to reassure safety). The most prevalent sleep/bedtime difficulties were fear/anxiety (23.2%), nightmares (19.6%), environment (18.4%). For example, one parent responded: “He fears never waking up. He fears that the sun is not going to come back up. He fears that the bad guys will come get him. ” Difficulties varied by age-- 4–5: fear/anxiety, destabilization from call/visit biological parent, nightmares; 6–9: nightmares, fear/anxiety, environment; 10–11: fear/anxiety, nightmares, emotional/behavioral difficulties. Most frequent parenting approaches were bedtime routine (63.0%), reading before bedtime (36.5%), physical reassurance (26.6%). Interventions also varied according to age-- 4–5: routine, reading, physical reassurance; 6–9: routine, reading, noise control; 10–11: routine, technology regulation, reading. Foster caregivers reported fear/anxiety as most common bedtime difficulty and physical reassurance as most frequent parenting strategy for healthy sleep. Findings suggest that anxiety and fear often interfere with sleep and, in turn, physical reassurance as a helpful bedtime strategy. Undoubtedly, there is a need for sleep research and preventive interventions for children in foster care. Summer Student Research Support, Loyola University Maryland, College of Arts and Sciences
Nonverbal Mechanisms Predict Zoom Fatigue and Explain Why Women Experience Higher Levels than Men

There is little data on Zoom Fatigue, the exhaustion that follows video conference meetings. This paper administers the Zoom Exhaustion & Fatigue scale to 10,591 participants from a convenience sample and tests the associations between five theoretical nonverbal mechanisms and Zoom Fatigue – mirror anxiety, being physically trapped, hyper gaze from a grid of staring faces, and the cognitive load from producing and interpreting nonverbal cues. First, we show that daily usage predicts the amount of fatigue, and that women have longer meetings and shorter breaks between meetings than men. Second, we show that women have greater Zoom fatigue than men. Third, we show that the five nonverbal mechanisms for fatigue predict Zoom fatigue. Fourth, we confirm that mirror anxiety mediates the difference in fatigue across gender. Exploratory research shows that race, age, and personality relate to fatigue. We discuss avenues for future research and strategies to decrease Zoom fatigue.
Effect of Supplemental Irrigation on Tuber Yield, Water Use Efficiency and Nitrogen Use Efficiency of Potato (Solanum Tuberosum L.) Grown in a Mollic Andosol

BackgroundThe yield of potato (8.86 tonnes/ha), the second staple food and cash crop in Kenya is remained low due to a reduction in seasonal precipitation and low soil fertility. Drought or dry periods between rainfall seasons and increased temperatures, which leads to high crop evapotranspiration, are experienced in 70-80% of the smallholder farms. Among major elements require by potato, nitrogen is the most important influential element but it is deficient in most potato-growing soils in Kenya because of nutrient depletion with inadequate nutrient replenishment results from continuous production. Hence the introduction of supplemental irrigation with an adequate application of this nutrient could increase crop yields. Therefore, this study was conducted in Nakuru, one of the major potato growing areas in Kenya, to determine the effects of full supplemental irrigation (FI) and four nitrogen levels, N0(0), N1(60), N2(90) and N3(130 kg N/ha) on tuber yield, water use efficiency (WUE) and nitrogen use efficiency (NUE) of potato grown in a mollic Andosol in Kenya compared to conventionnel rain-fed potato production. Results The results showed that tuber yield, marketable tuber yield and NUE significantly differed due to the interaction effect between irrigation and N-fertilization rate (P<0.001) whereas the WUE was statistically different due to the N-fertilization rate (P<0.001). The highest tuber yield 58.28 tonnes/ha was found in supplemental irrigation with an application of 130kg N/ha treatment. Full supplemental irrigation treatment increased marketable yield by 129.84, 94.63, 151.21 and 126.63% for 0, 60, 90 and 130 kg N/ha, respectively compared to rain-fed N-fertilization treatments. NUE increased statistically with an increase in N rate up to 90 kg N/ha, then tended to increase slightly as nitrogen rate increased further. An increase in potato tuber yield was positively correlated with number of tubers/plant (r=0.75), NUE (r=0.95), WUE (r=0.72) (P < 0.001). ConclusionsThe high potato yield and marketable tuber yield in mollic Andosol can be obtained when all water deficits of the growing season are eliminated with supplemental irrigation and an application of 130kg N/ha but it is essential to exploit water regimes for acceptable yield with water-saving.
An empirical investigation of enterprise system user satisfaction antecedents in Jordanian commercial banks

Abstract One substantial issue that is still unexplored enough in the academic literature is user satisfaction antecedents in the Enterprise System (ES) domain. To address this issue, this paper draws upon the Information System Success Model (ISSM) to explore the influence of system quality, information quality, and service quality on both perceived usefulness and user satisfaction, as well as the influence of perceived usefulness on user satisfaction. This research was done using a cross-sectional survey of ES users in Jordanian commercial banks. In total, 208 usable responses were analysed via Smart-PLS software. The empirical findings found substantial and significant support for all model propositions except for the relationship between service quality and user satisfaction, which was insignificant. This research is the first of its kind to test the factors that impact ES user satisfaction in Jordan. It is also among the first works that tested the factors that determine user satisfaction using ISSM for mandatory systems instead of voluntary systems. Finally, our findings offer policymakers and practitioners of Jordanian commercial banks insight into the factors that determine user satisfaction, thereby improving the effectiveness of these complex systems.
Forecasting UK inflation bottom up

We forecast CPI inflation in the United Kingdom up to one year ahead using a large set of monthly disaggregated CPI item series combined with a wide set of forecasting tools, including dimensionality reduction techniques, shrinkage methods and non-linear machine learning models. We find that exploiting CPI item series over the period 2011–19 yields strong improvements in forecasting UK inflation against an autoregressive benchmark, above and beyond the gains from macroeconomic predictors. Ridge regression and other shrinkage methods perform best across specifications that include item-level data, yielding gains in relative forecast accuracy of up to 70% at the one-year horizon. Our results suggests that the combination of a large and relevant information set combined with efficient penalisation is key for good forecasting performance for this problem. We also provide a model-agnostic approach to address the general problem of model interpretability in high-dimensional settings based on model Shapley values, partial re-aggregation and statistical testing. This allows us to identify CPI divisions that consistently drive aggregate inflation forecasts across models and specifications, as well as to assess model differences going beyond forecast accuracy.
Falcon fuel: metabarcoding reveals songbird prey species in the diet of juvenile Merlins (
 Falco columbarius
 ) migrating along the Pacific Coast of western North America

During autumn migration, bird-eating raptors are thought to rely on ﬂ ocks of migrant songbirds (Passeriformes) as a critical resource to fuel the energetic demands of long-distance migration. However, this hypothesis has been challenging to investigate, and the foraging ecology during migration of most migrant raptors remains unexplored. To address these knowledge gaps, our objective was to document the diet of a bird-eating falcon on active migration. We swabbed visible and trace prey residues from the exterior surface of beaks and talons of migrant juvenile Merlins Falco columbarius in the autumn of 2015 and 2016 at a raptor migration monitoring station positioned on the Paci ﬁ c Coast of western North America. We used a DNA metabarcoding approach and detected the presence of 40 distinct prey species derived from 210 individual prey species detections on 63 of the 72 (87.5%) migrant juvenile Merlins sampled. We detected an average of 3.3 (cid:1) 1.6 prey species on individual Merlins. We found that juvenile males selected smaller prey on average compared with juvenile females. Of the prey species detected, over 80% were migratory songbird species within the Paci ﬁ c Flyway. In 2015, we detected a greater proportion of irruptive migrants in juvenile Merlin diet compared with 2016. In 2016, we found that the proportion of annual migrants consumed by Mer-lins corresponded to the timing of peak annual songbird migration in the Paci ﬁ c Flyway. This study represents one of the ﬁ rst detailed descriptions of songbird prey species consumed by a migrating raptor and supports the hypothesis that migrating juvenile Merlins rely on migrant songbirds to support the energetic demands of migration.
Analytical Methods for Sulfur Determination in Glasses, Rocks, Minerals and Fluid Inclusions

The analytical techniques that are normally utilized to determine the concentration of sulfur in geologic materials can be divided into bulk analytical methods that require sample powders and microanalytical methods done in situ . The most common method of bulk sulfur analysis is accomplished using combustion methods followed by detection of SO2 in an infrared cell. A similar method involves an elemental analyzer coupled to a mass spectrometer where the mass 64 ion beam is monitored and compared to a standard. X-ray fluorescence is another method of bulk powder analysis that is well established for the determination of sulfur in coal and plant materials, but is only rarely used for the analysis of rocks due to a relatively high detection limit and difficulties with sample fusion. Micro-analytical techniques normally involve individual minerals or glasses. The most commonly used method is electron microprobe analysis (EMPA), but ion microprobe (SIMS) and laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) methods are useful for some samples. Nuclear methods of sulfur analyses are useful for some materials largely because of their very low detection limits. A relatively new technique is the application of LA-ICP-MS to determine the concentration of sulfur in fluid inclusions contained within glasses and minerals. In this chapter we review how these methods may be used to measure the concentration of sulfur in a variety of geologic materials. We emphasize the types of materials that can be analyzed, sample preparation, standardization techniques, detection limits and uncertainties that are associated with each of the methods. Elsewhere in this volume, the determination of sulfur species in glasses is discussed (Chapter 2 -- Wilke et al. 2011) and methods of isotopic analyses are treated (Chapter 15 -- Marini et al. 2011). ### Total sulfur concentration in minerals, rocks and glasses using elemental analyzer – infrared absorption technology Automated, high-temperature combustion, carbon-sulfur analyzers utilize solid state infrared (IR) absorption for the …
Gender Disparities in Post-Conflict Societies: A Cross-National Analysis

ABSTRACT This article investigates the impacts of conflict, resolution, and post-conflict democracy on gender bias. Exploring this question poses two methodological concerns. The first regards selection bias in which countries experience conflict and its resolution. The study addresses this issue using a generalization of the Heckman procedure. The second is that post-conflict democracy is likely endogenous to the level of pre-conflict democracy. This issue is addressed using two-stage least squares. Results show that conflict unambiguously worsens gender outcomes with respect to secondary school enrollment, labor force participation, fertility, and parliamentary representation. However, it does not affect the gap in life expectancy. Conflict resolution improves gender outcomes significantly, but not always by a magnitude that restores pre-conflict levels of equality. Greater post-conflict democratization improves parliamentary representation of women and the gender gaps in life expectancy and secondary school enrollment. However, it worsens the gap in labor force participation. HIGHLIGHTS The study corrects selection bias in conflict and its resolution with a three-step procedure. It instruments for post-conflict democratization using legal origin and geography. Conflict worsens gender inequities in education, the labor force, and representation. Conflict resolution mitigates most conflict-induced inequities, but not fully. Democratization further improves equity in representation and schooling.
Current state of artificial intelligence applications in ophthalmology and their potential to influence clinical practice

Abstract Artificial intelligence (AI) has emerged as a major frontier in healthcare and finds broad range of applications. It has the potential to revolutionize current procedures of disease diagnosis and treatment, thus influencing the clinical practice. Artificial intelligence (AI) in ophthalmology, primarily concentrates on diagnostic and treatment pathways for eye conditions such as cataract, glaucoma, age-related macular degeneration (MDA) and diabetic retinopathy (DR). The purpose of this article is to systematically review the existing state of literature on the various AI techniques and its applications in the diagnosis and treatment of eye diseases and conduct an in-depth enquiry to identify the challenges in accurate detection, pre-processing of data, monitoring and assessment through various AI algorithms. The results suggest that all AI models proposed reduce the detection time considerably. The potential limitations and challenges in the development and application play a significant role in clinical practice. There is a need for the development of AI-assisted technologies that shall consider the clinical implications based on experience and guided by patient-centred healthcare principles. The diagnostic models should assist ophthalmologists on making quick and accurate decisions in determining the progression of various ocular diseases.
Influence of permafrost thaw on an extreme geologic methane seep

The occurrence and magnitude of natural fossil methane (CH4) emissions in the Arctic are poorly known. Emission of geologic CH4, a potent greenhouse gas, originating beneath permafrost is of particular interest due to the potential for positive feedback to climate warming, whereby accelerated permafrost thaw releases permafrost‐trapped CH4 in a future warmer climate. The development of through‐going taliks in Arctic lakes overlying hydrocarbon reservoirs is one mechanism of releasing geologically sourced, subpermafrost CH4. Here we use novel gas flux measurements, geophysical observations of the subsurface, shallow sediment coring, high‐resolution bathymetry measurements, and lake water chemistry measurements to produce a synoptic survey of the gas vent system in Esieh Lake, a northwest Alaska lake with exceedingly large geologic CH4 seep emissions. We find that microbially produced fossil CH4 is being vented though a narrow thaw conduit below Esieh Lake through pockmarks on the lake bottom. This is one of the highest flux geologic CH4 seep fields known in the terrestrial environment and potentially the highest flux single methane seep. The poleward retreat of continuous permafrost may have implications for more subcap CH4 release with increased permafrost thaw.
Reputation System for Increased Engagement in Public Transport Oriented-Applications

Increasing user engagement is one of the biggest challenges when a new application is developed. An engaged user is one who finds a product valuable; highly engaged users generate profit. This study focuses on increasing user engagement in a transport application, via a user reputation score feature. The score is to reward application users and activity organisers, as well as to motivate beginners by offering a high reputation score in the first days of use. The algorithms are based on exponential and logarithmic functions, and were first tested on synthetic data. Real-world tests have shown that the algorithms behave as expected, but the COVID-19 pandemic created a disturbance which prevented any user from achieving the maximum score and many users from registering altogether. Data show positive results, although the real number of users is not sufficient to certify a correct behaviour. Further tests will be carried out when transport activities return to normal.
Analysis of Regional Distribution of Tree Species Using Multi-Seasonal Sentinel-1&2 Imagery within Google Earth Engine

Accurate information on tree species is in high demand for forestry management and further investigations on biodiversity and environmental monitoring. Over regional or large areas, distinguishing tree species at high resolutions faces the challenges of a lack of representative features and computational power. A novel methodology was proposed to delineate the explicit spatial distribution of six dominant tree species (Pinus tabulaeformis, Quercus mongolia, Betula spp., Populus spp., Larix spp., and Armeniaca sibirica) and one residual class at 10 m resolution. Their spatial patterns were analyzed over an area covering over 90,000 km2 using the analysis-ready large volume of multisensor imagery within the Google Earth engine (GEE) platform afterwards. Random forest algorithm built into GEE was used together with the 20th and 80th percentiles of multitemporal features extracted from Sentinel-1/2, and topographic features. The composition of tree species in natural forests and plantations at the city and county-level were performed in detail afterwards. The classification achieved a reliable accuracy (77.5% overall accuracy, 0.71 kappa), and the spatial distribution revealed that plantations (Pinus tabulaeformis, Populus spp., Larix spp., and Armeniaca sibirica) outnumber natural forests (Quercus mongolia and Betula spp.) by 6% and were mainly concentrated in the northern and southern regions. Arhorchin had the largest forest area of over 4500 km2, while Hexingten and Aohan ranked first in natural forest and plantation area. Additionally, the class proportion of the number of tree species in Karqin and Ningcheng was more balanced. We suggest focusing more on the suitable areas modeling for tree species using species’ distribution models and environmental factors based on the classification results rather than field survey plots in further studies.
On the performance of the snow model Crocus driven by in situ and reanalysis data at Villum Research Station in northeast Greenland

Abstract. Reliable and detailed snow data are limited in the Arctic. We aim at overcoming this issue by addressing two questions: (1) Can the reanalysis ERA5 replace limited in situ measurements in high latitudes to drive snow models? (2) Can the Alpine model Crocus simulate reliably Arctic snow depth and stratigraphy? We compare atmospheric in situ measurements and ERA5 reanalysis and evaluate simulated and measured snow depth, density and specific surface area (SSA) in northeast Greenland (October 2014–October 2018). To account for differences between Alpine and Arctic region, we introduce a new parametrisation for the density of new snow. Our results show a good agreement between in situ and ERA5 atmospheric variables except for precipitation, wind speed and direction. ERA5’s resolution is too coarse to resolve the topography in the study area adequately, leading presumably to the detected biases. Nevertheless, measured snow depth agrees better with ERA5 forced simulations than forced with in situ measurements. Crocus can simulate satisfactory the evolution of snow depth, but simulations of SSA and density profiles for both forcings are biased compared to field measurements. Adjusting the new snow density parametrisation leads to improvements in the simulated snow stratigraphy. In conclusion, ERA5 can be used instead of in situ measurements to force snow models but the use of Crocus in the Arctic is affected by limitations likely due to the missing vertical water vapour transport and snow redistribution during strong winds. These limitations strongly affect the accuracy of the vertical profiles of physical snow properties.
Label Noise Detection System Against Label Flipping Attack

The label flipping attack is a special poisoning attack in the adversarial environment. By adding noise to the data, it destroys the learning process of the model and affects the decision-making performance of the model. Recent literature work uses semi-supervised learning techniques to defend against label flipping attacks. However, these methods require a clean dataset to achieve their goals. This study proposes a novel label noise processing framework to correct the labels of contaminated samples in the data preprocessing stage. Based on five real UCI datasets, we evaluated the effectiveness of the semi-supervised defensive label noise correction algorithm based on the AdaBoost algorithm(AdaSSL). With a noise ratio of 0~20%, we compared the classification performance of six classic machine learning algorithms (NB, LR, SVM, DT, KNN and MLP) under the AdaSSL defense algorithm. The results show that compared with the most advanced semi-supervised defense algorithms in the literature, our algorithm does not need to use an additional dataset. At a noise ratio of 10%, the AdaSSL algorithm is significantly better than state of the art label noise defense technology.