Miracle-dz/UniFAR-FTUs huggingface dataset:
The Brain and Early Experience Study: Protocol for a Prospective Observational Study

Background Children raised in conditions of poverty (or near poverty) are at risk for nonoptimal mental health, educational, and occupational outcomes, many of which may be precipitated by individual differences in executive function (EF) skills that first emerge in early childhood. Objective The Brain and Early Experience study considers prenatal and postnatal experiences that may mediate the association between poverty and EF skills, including neural substrates. This paper described the study rationale and aims; research design issues, including sample size determination, the recruitment strategy, and participant characteristics; and a summary of developmental assessment points, procedures, and measures used to test the study hypotheses. Methods This is a prospective longitudinal study examining multiple pathways by which poverty influences normative variations in EF skills in early childhood. It is funded by the National Institute of Child Health and Human Development and approved by the institutional review board. Results Recruitment is complete with a sample of 203 participants, and data collection is expected to continue from September 2018 to February 2024. Of those recruited as low socioeconomic status (SES), 71% (55/78) reported income-to-needs (ITN) ratios of <2.0, and 35% (27/78) reported ITN ratios of <1.0. Among participants recruited into the not-low SES stratum, only 8.8% (11/125) reported ITN ratios of <2.0, and no participant reported ITN ratios of <1.0. The average ITN ratio for participants recruited into the low-income stratum was significantly lower than the average for the high-income recruitment cell (P<.001). Comparable recruitment outcomes were observed for both Black and non-Black families. Overall, the sample has adequate diversity for testing proposed hypotheses, with 13.3% (27/203) of participants reporting ITN ratios of <1 and >32.5% (66/203) reporting ratios of <2.0. Conclusions Preliminary results indicate that the recruitment strategy for maximizing variation in family SES was successful, including variation within race. The findings of this study will help elucidate the complex interplay between prenatal and postnatal risk factors affecting critical neurocognitive developmental outcomes in early childhood. International Registered Report Identifier (IRRID) DERR1-10.2196/34854
The conformal Yamabe constant of product manifolds

Let (V; g) and (W; h) be compact Riemannian manifolds of dimension at least 3. We derive a lower bound for the conformal Yamabe constant of the product manifold (V x W; g + h) in terms of the conformal Yamabe constants of (V; g) and (W; h).
Precautionary Savings and Global Imbalances in World General Equilibrium

In this paper we assess the implications of precautionary savings for global imbalances by considering a world economy model composed by the US, the Euro Area, Japan, China, oil-exporting countries, and the rest of the world. These areas are assumed to differ only with respect to GDP volatility which is calibrated based on the 1980-2008 period. The model predicts a wide dispersion in net foreign asset positions, with the highly volatile oil-exporting countries accumulating very large asset holdings. While heterogeneity in GDP volatility may lead to large imbalances in international investment positions, its impact on current accounts is much weaker. This is because countries are expected to move towards their optimal NFA at a very slow pace.
THE RADIAL DISTRIBUTION OF STAR FORMATION IN GALAXIES AT z ∼ 1 FROM THE 3D-HST SURVEY

The assembly of galaxies can be described by the distribution of their star formation as a function of cosmic time. Thanks to the WFC3 grism on the Hubble Space Telescope (HST) it is now possible to measure this beyond the local Universe. Here we present the spatial distribution of Hα emission for a sample of 54 strongly star-forming galaxies at z ∼ 1 in the 3D-HST Treasury survey. By stacking the Hα emission, we find that star formation occurred in approximately exponential distributions at z ∼ 1, with a median Sérsic index of n = 1.0 ± 0.2. The stacks are elongated with median axis ratios of b/a = 0.58 ± 0.09 in Hα consistent with (possibly thick) disks at random orientation angles. Keck spectra obtained for a subset of eight of the galaxies show clear evidence for rotation, with inclination corrected velocities of 90–330 km s−1. The most straightforward interpretation of our results is that star formation in strongly star-forming galaxies at z ∼ 1 generally occurred in disks. The disks appear to be “scaled-up” versions of nearby spiral galaxies: they have EW(Hα) ∼ 100 Å out to the solar orbit and they have star formation surface densities above the threshold for driving galactic scale winds.
Quasi-periodic Fast-mode Magnetosonic Wave Trains Within Coronal Waveguides Associated with Flares and CMEs

Quasi-periodic, fast-mode, propagating wave trains (QFPs) are a new observational phenomenon recently discovered in the solar corona by the Solar Dynamics Observatory with extreme ultraviolet (EUV) imaging observations. They originate from flares and propagate at speeds up to ∼2000 km s−1 within funnel-shaped waveguides in the wakes of coronal mass ejections (CMEs). QFPs can carry suffcient energy fluxes required for coronal heating during their occurr ences. They can provide new diagnostics for the solar corona and their associated flares. We present recent observations of QFPs focusing on their spatio-temporal properties, temperature dependence, and statistical correlation with flares and CMEs. Of particular interest is the 2010-Aug-01 C3.2 flare with correlated QFPs and drifting zebra and fiber radio bursts, which might be different manifestations of the same fast-mode wave trains. We also discuss the potential roles of QFPs in accelerating and/or modulating the solar wind.
The Design Of Complex Systems To Resist The EffectsOf A Nuclear Explosion

The design of complex systems subject to the effects of a nuclear explosion is a critical issue, particularly due to the variety of phenomena generated by the explosion. The major effects which are to be considered in a low altitude nuclear explosion include: mechanical effects, thermal effects, nuclear radiations, and electromagnetic pulse. The discipline concerned with analysis, design and production of systems to resist the effects of a nuclear explosion is termed Nuclear Hardening. The paper provides an overview on the effects of a nuclear explosion and on the consequences that they can have on complex systems. The methodology applicable to weapon systems such as tanks, armoured vehicles and war ships, is described.
Seamless Integration of the Coastal Ocean in Global Marine Carbon Cycle Modeling

We present the first global ocean‐biogeochemistry model that uses a telescoping high resolution for an improved representation of coastal carbon dynamics: ICON‐Coast. Based on the unstructured triangular grid topology of the model, we globally apply a grid refinement in the land‐ocean transition zone to better resolve the complex circulation of shallow shelves and marginal seas as well as ocean‐shelf exchange. Moreover, we incorporate tidal currents including bottom drag effects, and extend the parameterizations of the model's biogeochemistry component to account explicitly for key shelf‐specific carbon transformation processes. These comprise sediment resuspension, temperature‐dependent remineralization in the water column and sediment, riverine matter fluxes from land including terrestrial organic carbon, and variable sinking speed of aggregated particulate matter. The combination of regional grid refinement and enhanced process representation enables for the first time a seamless incorporation of the global coastal ocean in model‐based Earth system research. In particular, ICON‐Coast encompasses all coastal areas around the globe within a single, consistent ocean‐biogeochemistry model, thus naturally accounting for two‐way coupling of ocean‐shelf feedback mechanisms at the global scale. The high quality of the model results as well as the efficiency in computational cost and storage requirements proves this strategy a pioneering approach for global high‐resolution modeling. We conclude that ICON‐Coast represents a new tool to deepen our mechanistic understanding of the role of the land‐ocean transition zone in the global carbon cycle, and to narrow related uncertainties in global future projections.
EVALUATION EDUCATIONAL SERVICE QUALITY OF PHYSICAL EDUCATION AND SPORT SCIENCES FACULTY OF THE UNIVERSITY OF GUILAN ACCORDING TO SERVQUAL MODEL

The quality of educational services is determined by investigating the gap between student’s expectations and provided educational services. This research was following to answer this question: how is the educational services quality in faculty of physical education and sport science of University of Guilan? To data collection the SERVQUAL questionnaire with 5 dimensions . Content validity of the questionnaire was investigated in viewpoint of experts and reliability of it was calculated with Croanbach’s alpha coefficient. Confirmatory factor analysis was used to determine concept validity.The results showed the perceived dimensions between girls and boys have no significant difference. All of the dimensions of educational services quality have negative and significant gaps.Negative gaps means, the educational services quality were not in the expectation of students and so essential actions must be done to meet the student’s expectations. Keywordsevaluation, educational services quality, SERVQUAL, perception, expectation
Azimuthally polarized cathodoluminescence from InP nanowires

We determine the angle and polarization dependent emission from 1.75 µm and 2.50 µm long InPnanowires by using cathodoluminescencepolarimetry. We excite the vertical wires using a 5 keV electron beam, and find that the 880 nm bandgap emission shows azimuthally polarized rings, with the number of rings depending on the wire height. The data agree well with a model in which spontaneous emission from the wire emitted into the far field interferes with emission reflected off the substrate. From the model, the depth range from which the emission is generated is found to be up to 400 nm below the top surface of the wires, well beyond the extent of the primary electron cloud. This enables a probe of the carrier diffusion length in the InPnanowires.
Scan Line Intensity-Elevation Ratio (SLIER): An Airborne LiDAR Ratio Index for Automatic Water Surface Mapping

Owing to the characteristics of how a laser interacts with the water surface and water column, the measured Light Detection and Ranging (LiDAR) intensity values are different with respect to the laser wavelength, the scanning geometry and the reflection mechanism. Depending on the instantaneous water condition and the laser incidence angle, laser dropouts can appear, causing null returns or empty holes found in the collected LiDAR data. This variable intensity response offers a valuable opportunity for using airborne LiDAR sensors for automatic identification of water regions, and thus, we previously proposed an airborne LiDAR-based ratio index named the scan line intensity-elevation ratio (SLIER). Over the water surface, airborne LiDAR data are always found to have a high fluctuation of the intensity value and low variation of the elevation along each scan line, and thus, the water region has a higher SLIER value compared to the land. We examined the SLIER on a multispectral airborne LiDAR dataset collected by Optech Titan and a monochromatic airborne LiDAR dataset collected by Optech Galaxy on a natural rocky shore and a man-made shore. Our experiments showed that SLIER was able to provide a high separability between land and water regions and was able to outperform the traditional normalized difference water index (NDWI) for estimation of the water surface. With the use of SLIER as a mechanism for training data selection, our case studies demonstrated an overall accuracy of 98% in the use of either monochromatic or multispectral LiDAR data, regardless of the laser channel being used.
Brain Response and Reaction Time in Natural and Comfort Conditions, with Energy-Saving Potential in an Office Environment

Psychological adaptation to ambient temperatures is fascinating and critical, both theoretically and practically, for energy efficiency in temperate climates. In this study, we investigated and compared the brain response (event-related potentials with a late positive component and latency ~300 milliseconds; labeled “P300” in the present study) and reaction times of Indonesian participants (n = 11), as tropical natives living in Japan, and Japanese participants (n = 9) in natural (i.e., hot during the summer and cold during the winter) and comfort conditions (with cooling and heating). Thermal comfort under contrasting conditions was studied using both instruments and subjective ratings. P300 potential and reaction time were measured before and after a Uchida–Kraepelin (U–K) test (30 summation lines). The results showed that P300 potential and latency did not change between the pre- and post-U–K test among conditions in any of the groups. Furthermore, Indonesian participants showed lower P300 potential (hot conditions) and slower P300 latency (hot and cooling conditions) than Japanese participants. We also found that the reaction time of the Indonesian group significantly differed between the pre- and post-U–K test in an air-conditioned environment, with either cooling or heating. In this study, Indonesian participants demonstrated a resistance to P300 and worse reaction times during work in a thermally unfamiliar season, specifically indicated by the indifferent performances among contrasting environmental conditions. Indonesian participants also showed similar thermal and comfort sensations to Japanese participants among the conditions. In the winter, when the Indonesian neutral temperature is higher than Japanese’s, the energy consumption may increase.
A Grounded Unsupervised Universal Part-of-Speech Tagger for Low-Resource Languages

Unsupervised part of speech (POS) tagging is often framed as a clustering problem, but practical taggers need to ground their clusters as well. Grounding generally requires reference labeled data, a luxury a low-resource language might not have. In this work, we describe an approach for low-resource unsupervised POS tagging that yields fully grounded output and requires no labeled training data. We find the classic method of Brown et al. (1992) clusters well in our use case and employ a decipherment-based approach to grounding. This approach presumes a sequence of cluster IDs is a ‘ciphertext’ and seeks a POS tag-to-cluster ID mapping that will reveal the POS sequence. We show intrinsically that, despite the difficulty of the task, we obtain reasonable performance across a variety of languages. We also show extrinsically that incorporating our POS tagger into a name tagger leads to state-of-the-art tagging performance in Sinhalese and Kinyarwanda, two languages with nearly no labeled POS data available. We further demonstrate our tagger’s utility by incorporating it into a true ‘zero-resource’ variant of the MALOPA (Ammar et al., 2016) dependency parser model that removes the current reliance on multilingual resources and gold POS tags for new languages. Experiments show that including our tagger makes up much of the accuracy lost when gold POS tags are unavailable.
Application of Harmony Search Algorithm to Slope Stability Analysis

Slope stability analysis is undoubtedly one of the most complex problems in geotechnical engineering and its study plays a paramount role in mitigating the risk associated with the occurrence of a landslide. This problem is commonly tackled by using limit equilibrium methods or advanced numerical techniques to assess the slope safety factor or, sometimes, even the displacement field of the slope. In this study, as an alternative approach, an attempt to assess the stability condition of homogeneous slopes was made using a machine learning (ML) technique. Specifically, a meta-heuristic algorithm (Harmony Search (HS) algorithm) and K-means algorithm were employed to perform a clustering analysis by considering two different classes, depending on whether a slope was unstable or stable. To achieve the purpose of this study, a database made up of 19 case studies with 6 model inputs including unit weight, intercept cohesion, angle of shearing resistance, slope angle, slope height and pore pressure ratio and one output (i.e., the slope safety factor) was established. Referring to this database, 17 out of 19 slopes were categorized correctly. Moreover, the obtained results showed that, referring to the considered database, the intercept cohesion was the most significant parameter in defining the class of each slope, whereas the unit weight had the smallest influence. Finally, the obtained results showed that the Harmony Search algorithm is an efficient approach for training K-means algorithms.
An Improved Defogging Algorithm Based on Dark Color Theory Combined with Self-Adaptive Threshold Mechanism

Defogging algorithms based on dark channel prior have color shift in light color areas because of inaccurate estimation of transmittance. To resolve this problem, a novel improved image clearness method is proposed. Based on the dark channel prior, the essential causes of color shift are analyzed, with two important factors summarized. Then, transmission map is calculated by using 3⁎3 fixed region, and the restoration module based on self-adaptive threshold mechanism for transmission map is provided. Some experiments are carried out to determine parameters in restoration module to correct the transmission map. According to the corrected transmission map, a transmission restoration algorithm is constructed based on the self-adaptive threshold mechanism to improve the performance of the fog-free image. The experiment results show that this method can resolve the color shift in light color areas effectively and guarantee the overall framework of defogging method based on dark color theory unchanged.
Impact of Tumbling Process on the Toughness and Structure of Raw Beef Meat Pieces

Tenderness is a major factor in consumer perception and acceptability of beef meat. Here we used a laboratory tumbling simulator to investigate the effectiveness of the tumbling process in reducing the toughness of raw beef cuts. Twelve Semitendinosus beef muscles from cows were tumbled according to four programs: T1 (2500 consecutive compression cycles (CC), for about 3 h), T2 (6000 CC, about 7.5 h), T3 (9500 CC, about 12 h), and T4 (13,000 CC, about 16 h). The effect of tumbling on the toughness of raw meat was assessed using compression tests (stresses measured at 20% and 80% of deformation ratios) and microscopic observations made at the periphery and centre of meat samples, and compared against non-tumbled controls. Longer tumbling times significantly reduced the stresses measured at 20% and 80% compression rates, which reflected the toughness of muscle fibres and connective tissue, respectively. At the microscopic level, longer tumbling times led to reduced extracellular spaces, increased degradation of muscle structure, and the emergence of amorphous zones. A 12-h tumbling protocol ultimately makes the best compromise between the process time demand and toughness reduction in beef Semitendinosus meat pieces.
Electron transport in CdTe/ZnO radial nanowire array solar cells

This project was conducted during the spring of 2013 at the IMEP-LAHC laboratory in Grenoble, as part of their research into the electrical and optical properties of ZnO/CdTe-based radial nanowire array solar cells. Simulations with different transport mechanisms and physical properties of the semiconductors were made and compared to experimentally acquired data. It was confirmed that the nanowire array setup is advantageous compared to a conventional planar structure, and it was also found that quantum mechanical tunneling of electrons through crystal defects or impurities in the core/shell interface could be the main dark current transport mechanism.
Design of an Automated Multiposition Dynamic Wheelchair

This work presents a design for an automatized multiposition dynamic wheelchair used to transport quadriplegic patients by reconfiguring a manual wheelchair structure. An electric actuator is attached to a four-bar mechanism fixed to each side of a wheelchair’s backrest to reach multiposition. The entire device is actuated through a PID controller. An experimental test is carried out in a simplified wheelchair structure. Finally, the structure of the wheelchair is evaluated through the Dynamic analysis and Finite Element Method under the payload computed with the most critical position reached by the mechanism.
ELECTRICAL CHARACTERISTICS AND PROPERTIES OF A STUB ( DAMAGED TOUGHENED GLASS INSULATOR )

Overhead line insulators can be damaged for various reasons during their service life. Porcelain or composite insulators once damaged can display a large variety of conditions and aspects. On the other hand, damaged toughened glass insulators always appear as a stub thanks to the properties of toughening. While the mechanical residual strength of a stub is covered by standard tests, its electrical performance is not always fully understood by the users. This paper presents a comprehensive approach to the electrical characteristics of a glass stub taken individually or in a string of insulators. A model of a stub is being proposed in an equivalent electrical circuit. Test results on stubs tested individually or in full strings are analysed and will help to understand their behaviour in situations in line with normal service conditions as well as during over voltages. The influence of environmental elements (dry, wet or humid conditions) is also presented through selected pre-stress conditions. Finally, the electrical behaviour and characteristics of a stub are studied after mechanical and thermal mechanical preconditioning. The electrical properties of a stub offer a significant contribution to the utilities in their maintenance, inspection and live line work strategy.
When Algorithms Import Private Bias into Public Enforcement: The Promise and Limitations ofStatistical Debiasing Solutions

We make two contributions to understanding the role of algorithms in regulatory enforcement. First, we illustrate how big-data analytics can inadvertently import private biases into public policy. We show that a much-hyped use of predictive analytics - using consumer data to target food-safety enforcement - can disproportionately harm Asian establishments. Second, we study a solution by Pope and Sydnor (2011), which aims to debias predictors via marginalization, while still using information of contested predictors. We find the solution may be limited when protected groups have distinct predictor distributions, due to model extrapolation. Common machine-learning techniques heighten these problems.