Miracle-dz/UniFAR-FTUs huggingface dataset:
The Factorial Structure of Spatial Abilities in Russian and Chinese Students

Background: Recent research suggested a unifactorial structure of spatial ability (SA). Research is needed to replicate this finding in different populations. Objective: This study aims to explore the factorial structure of SA in samples of 921 Russian and 229 Chinese university students. Design: A gamified spatial abilities battery was administered to all participants. The battery consists of 10 different domains of SA, including 2D and 3D visualization, mental rotation, spatial pattern assembly, spatial relations, spatial planning, mechanical reasoning, spatial orientation and spatial decision making speed and flexibility. Results: The results of the factor analysis showed a somewhat different pattern for different samples. In the Russian sample, the unifactorial structure, shown previously in a large UK sample (Rimfeld et al., 2017), was replicated. A single factor explained 40% of the variance. In the Chinese sample two factors emerged: first factor explained 26% of the variance and the second factor, including only Mechanical reasoning and Cross-Sections tests, explained 14%. The results also showed that the Chinese sample significantly outperformed the Russian sample in 5 out of the 10 tests. Russian students showed better performance only in two of the tests. The effects of all group comparisons were small. Conclusion: Overall, a similar amount of variance in the 10 tests was explained in the two samples, replicating results from the UK sample. Future research is needed to explain the observed differences in the structure of SA.
Development and Statistical Modeling of Dirt Resistive Latex Façade Paint

A highly dirt resistant paint for building facades without chemicals harmful to nature and environment, is developed which resolves the unattractive disfigurement of building walls caused by dirt. The experimentation is scientifically and statistically planned with the aid of computer programming. It consists of a sequence of phases which include the selection of appropriate raw materials, adopting of Basic Language computer programming to generate a target population of paint formulations. The average PVC percentage is computed using theory and found to be 54.98% for the target population of 543143 paint formulations hence verifies the literature results. Experimentation and statistical analysis are performed to compare the classical conventional agitator with latest lab equipment like Nano mill and it is concluded that Nano mill performs better on the average than conventional agitator in preparation of paint formulations. Hence the sample of paint formulations is prepared on Nano mill and tested in laboratory using advanced available technology for the analysis and comparison of paint properties to determine the best paint formulation. The results are analyzed using Analysis Of Variance Technique (ANOVA) and it is concluded that the paint formulation named “O3” has the highest dirt resistance on the average. The final selected formula O3 is compared with three other competitor paints in market under natural environment for a period of almost one year. A regression model is also constructed to study the effect of environmental factors like time, temperature and humidity on dirt resistance of paints. It is found that O3 formulation is the best environment friendly which performs equally well with one competitor paint and has higher dirt resistance than two other competitor paint formulations containing harmful chemicals. The regression model of dirt resistance on variables including time, temperature and humidity shows that these factors are significantly affecting the dirt resistance of a given paint at 5% level of significance. 95.34% variation in the dirt resistance of a given paint is due to and explained by the given factors. The regression model is useful to predict the average dirt resistance of a given paint with a certain level of confidence. The project exemplifies the work of an applied research from conceptualization to successful commercialization for the paint industry.
The Real Effects of Household Debt in the Short and Long Run

Household debt levels relative to GDP have risen rapidly in many countries over the past decade. We investigate the macroeconomic impact of such increases by employing a novel estimation technique proposed by Chudik et al (2016), which tackles the problem of endogeneity present in traditional regressions. Using data on 54 economies over 1990-2015, we show that household debt boosts consumption and GDP growth in the short run, mostly within one year. By contrast, a 1 percentage point increase in the household debt-to-GDP ratio tends to lower growth in the long run by 0.1 percentage point. Our results suggest that the negative long-run effects on consumption tend to intensify as the household debt-to-GDP ratio exceeds 60%. For GDP growth, that intensification seems to occur when the ratio exceeds 80%. Finally, we find that the degree of legal protection of creditors is able to account for the cross-country variation in the long-run impact.
A Comparison of Regression and Artificial Intelligence Methods in a Mass Appraisal Context

The limitations of traditional linear multiple regression analysis (MRA) for assessing value of real estate property have been recognized for some time. Artificial intelligence (AI) based methods, such as neural networks (NNs), have been studied in an attempt to address these limitations, with mixed results, weakened further by limited sample sizes. This paper describes a comparative study where several regression and AI-based methods are applied to the assessment of real estate properties in Louisville, Kentucky, U.S.A. Four regression-based methods (traditional MRA, and three non-traditional regression-based methods, Support Vector Machines using sequential minimal optimization regression (SVM-SMO), additive regression, and M5P trees), and three AI-based methods (NNs, radial basis function neural network (RBFNN), and memory-based reasoning (MBR)) have been applied and compared under various simulation scenarios. The results, obtained using a very large data sample, indicate that non-traditional regression-based methods perform better in all simulation scenarios, especially with homogeneous data sets. AI-based methods perform well with less homogeneous data sets under some simulation scenarios.
A Food Recommender for Patients in a Care Facility

In this research, a food recommendation strategy for patients in a care facility is proposed. Since many of these patients cannot express their personal preferences, a recommender system can assist the caregivers in the selection of the menu items that match the patients’ preferences. Recommendations are generated based on three information sources: explicit ratings for menu items, implicit feedback based on the patient’s eating behavior and the amount of food that was eaten, and inferred preferences for the ingredients of the menu items. In addition, monitoring the amount of food that was eaten by each patient can provide insights into the optimal amount of each menu item that has to be served to each patient. Furthermore, monitoring food consumption allows to detect irregularities in the eating behavior of the patient, which can be a symptom of illness.
An Exploration of Factors Affecting Drivers’ Daily Fuel Consumption Efficiencies Considering Multi-Level Random Effects

This paper investigates the factors affecting drivers’ vehicle fuel consumption efficiency, which was defined as the daily average fuel consumption for a unit of driving mileage. Based on the long-term Controller Area Network (CAN) data collected from private cars during 10 months in Toyota City, Japan, we explored the relationships between drivers’ fuel consumption efficiencies, and factors including drivers’ characteristics, car attributes, date-specific environmental attributes, and travel behavior. Furthermore, a multi-level model was applied to explicitly incorporate the effects of individual-specific, date-specific, and observation-specific unobserved factors. According to the estimation results, it was found that, on working days, model fit was significantly enhanced by incorporating all three error terms. Several findings regarding the relationships between observed factors and drivers’ fuel consumption efficiencies were also obtained.
2 Previous Work 2 . 1 Neural Language Modeling with LSTMs

Neural language models (LMs) are typically trained using only lexical features, such as surface forms of words. In this paper, we argue this deprives the LM of crucial syntactic signals that can be detected at high confidence using existing parsers. We present a simple but highly effective approach for training neural LMs using both lexical and syntactic information, and a novel approach for applying such LMs to unparsed text using sequential Monte Carlo sampling. In experiments on a range of corpora and corpus sizes, we show our approach consistently outperforms standard lexical LMs in character-level language modeling; on the other hand, for word-level models the models are on a par with standard language models. These results indicate potential for expanding LMs beyond lexical surface features to higher-level NLP features for character-level models.
Language Impairment : A Neuropsychological Characterization

This study’s objective was to characterize the neuropsychological functioning of children with Specifi c Language Impairment (SLI), in the areas of visual and verbal memory, attention/executive functions, and visual-perceptual functions. The sample consisted of 28 children with SLI and 28 children without SLI. Both groups were comparable in terms of age, gender, school grade and socioeconomic level. The assessment instruments used are part of Coimbra’s Neuropsychological Assessment Battery and data were analyzed through parametric and nonparametric statistical tests. In general, the results differentiate the performances of children with SLI from those observed in children without SLI. In comparison to the control group, the group of children with SLI obtained poorer results in visual memory and verbal memory, selective and sustained attention, executive functions, semantic verbal fl uency, visual-perceptual functions and processing speed.
DECISION SUPPORT SYSTEM CLASSIFICATION AND ITS APPLICATION IN MANUFACTURING SECTOR: A REVIEW

The purpose of this paper is to review decision support system application trend in manufacturing sector. Following the introduction of decision support system, the paper has discussed the application of decision support system in manufacturing sector and identifies the trend in term of decision support system types and their application types. In year 2011 until 2015, the most preferred decision support system were developed by using the model application. It also been found that, most of the developed decision support system are used to support evaluation activities in manufacturing operations. This review provides research trend on decision support system for the recent five years (2011 -2015) in the context of decision support system application in manufacturing industry.
Identification of submersible pump temperature changes model using KDD methods

This paper deals with the problem of the autoregressive model identification using KDD methods. In the considered problem, the autoregressive models are applied to describe dynamics processes of various technical systems. In particular, a method of functional dependencies discovering was presented. The method was designed for exploring data sets gathered by industrial SCADA systems. For the problem of the identification of pump temperature changes model, the method was verified. For this particular reason, a set of data was used which was gathered by submersible pumping station SCADA system. The assumptions, the exemplary results of the conducted research and conclusions were presented, as well.
Studying the Drug Delivery Kinetics of Nanosponges Using a MIP-Based Thermal Sensing Platform

The implementation of Molecularly Imprinted Polymers (MIPs) into sensing systems has 14 been demonstrated abundantly over the past decades. In this article, a novel application for a MIP15 based thermal sensing platform is introduced by using the sensor to characterize the drug release 16 kinetics of a nano-sized silver-organic framework. This so-called Ag-nanosponge was loaded with 17 acetylsalicylic acid (aspirin) which was used as a model drug compound in this study. The drug 18 elution properties were studied by placing the nanosponge in phosphate buffered saline solution 19 for two days and measuring the drug concentration at regular time intervals. To this extent, an 20 acrylamide-based MIP was synthesized that was able to detect aspirin in a specific and selective 21 manner. Rebinding of the template to the MIP was analyzed using a thermal sensor platform. The 22 results illustrate that addition of aspirin into the sensing chamber leads to a concentration23 dependent increase in the phase shift of a thermal wave that propagates through the MIP-coated 24 sensor chip. After constructing a dose-response curve, this system was used to study the drug 25 release kinetics of the nanosponge, clearly demonstrating that the metalorganic framework releases 26 the drug steadily over the course of the first hour after which the concentration reaches a plateau. 27 These findings were further confirmed by UV-VIS spectroscopy, illustrating a similar time28 dependent release in the same concentration range, which demonstrates that the MIP-based 29 platform can be used as a low-cost, straightforward tool to assess the efficacy of drug delivery 30 systems in a lab environment. 31
Spontaneous CP violation in the 2HDM: physical conditions and the alignment limit

For the general Two-Higgs-Doublet model, we present conditions for having spontaneous CP violation, in terms of physical masses and couplings. These relations involve the charged-Higgs mass, its cubic couplings with neutral scalars and quartic coupling, and become particularly simple in the alignment limit. In the simplified model with softly broken $Z_2$ symmetry, some deviation from alignment is required for spontaneous CP violation to be present.
Accident Thermal Loading Effects on Seismic Behaviour of Safety-Related Nuclear Structures

The Fukushima event of 2011 has highlighted the importance of designing safety-related nuclear facilities for accident thermal scenarios combined with design basis (safe shutdown) earthquake. While the probability of both events occurring simultaneously is low, severe environmental conditions may trigger accident thermal loading, and subsequent aftershocks, potentially as intense as the main shock, may occur during the accident thermal event. Current design codes and standards in the United States and abroad provide little-to-no guidance for including the effects of accident thermal loading on seismic behaviour (stiffness, strength, ductility or reserve margin) of structures. Prior research has focused on seismic behaviour or accident thermal loading but not both in combination. This is valid for both existing conventional reinforced concrete (RC) and modern steel-plate composite (SC) structures. The authors have initiated a research project focussing on the effects of accident thermal scenarios on the in-plane shear behaviour (stiffness and strength) of SC and RC wall structures. This paper presents the initial findings from the project including: (i) typical temperature-time (T-t) curves for containment internal structures in pressurized water reactors, (ii) thermal gradient histories that develop through the concrete thickness, (iii) concrete cracking due to the severe gradient and internal restraint, (iv) in-plane shear behaviour of the wall after concrete cracking, and (v) effects of external restraints on the in-plane shear behaviour. The paper presents these findings related to both RC and SC walls, which are being used to develop the test matrix and parameters to be included in the experimental investigations that will be conducted in the next phase of the project.
High Resolution Satellite Image Enhancement Using Transform Techniques

Nowadays satellite images are used in many applications such as geoscience studies, astronomy, geographical information systems and defense monitoring. The major problem of these types of images is their resolution because the resolution of these images varies depends on the instrument used and the altitude of the satellite's orbit. A new satellite image resolution enhancement technique has been proposed based on the Wrapping technique of Discrete Curvelet Transform (DCvT), which represents edges better than wavelets. In this, an input image is transformed into frequency domain by applying 2D FFT. The product of image and curvelet in frequency domain is obtained and wrapped around the origin. Finally, the DCvT coefficients are generated by applying the inverse 2D FFT to the wrapped data. The proposed technique is tested on satellite benchmark images. The quantitative measures (peak signal-to-noise ratio, mean absolute error and mean square error) and visual results show the superiority of this technique over the other image resolution enhancement techniques.
NimbRo @Home 2010 Team Description

This document describes the RoboCup@Home league team NimbRo of Rheinische Friedrich-Wilhelms-Universität Bonn, Germany, for the competition to be held in Singapore in June 2010. Our team uses self-constructed humanoid robots for object manipulaion and intuitive multimodal communication with humans. The paper describes the mechanical and electrical design of our robots Dynamaid and Robotinho. It also covers perception and behavior control.
Laser beam cleanup using improved model-based wavefront sensorless adaptive optics

An improved model-based wavefront sensorless adaptive optics algorithm is proposed for laser beam cleanup. Deformable mirror (DM) eigenmodes are used to replace traditional Lukosz modes in order to avoid DM fitting errors. The traditional method is based on a sophisticated calibration process and solving linear equations. In our method, coefficients of DM eigenmodes are estimated by adding bidirectional modal biases into the system and then solving parabolic equations. The calibration process is omitted in our method, which makes it more feasible. From simulation and experimental results, the corrective accuracy of the improved method is higher than the traditional one.
Trend Analysis in Ecological Status and Macrophytic Characterization of Watercourses: Case of the Semois-Chiers Basin, Belgium Wallonia

In order to analyze the distribution and evolution of the aquatic vegetation and ecological status of the Semois-Chiers basin (Semois sub-basin and Chiers sub-basin), macrophyte surveys were conducted at 48 sites in 2007 and 2013. Environmental parameters were also measured in order to characterize the waterbodies in terms of physico-chemical properties and anthropogenic pressure. The two-way clustering and indicator value (INDVAL) methods were used to assess groups of sites according to their macrophytic composition and species communities. The results showed a clear difference between streams in the Lorraine area (calcareous watercourses) and in the Ardennes (siliceous). Within each natural region, those with natural vegetation of high ecological status were separated from those dominated by resistant species. The Macrophytical Biological Index for Rivers (IBMR) was calculated for the sites visited in 2010 and 2013 and the results show a trend towards an increase of IBMR values of polluted sites. For the latter, the Wilcoxon test was performed to assess the significance of the difference in quality between 2010 and 2013. This showed a statistically significant difference (p-value = 0.035). Our results showed similarities with previous data, as well as some differences. The differences observed might indicate a gradual change in the composition of the vegetation in the study area, which was caused by changes in environmental conditions. They could also reflect a lack of information about the ecology of certain groups of plants, mainly bryophytes and macroalgae that were not considered in previous studies. Despite the measures implemented under the EU’s Water Framework Directive (WFD), the current vegetation of the Semois river differs little from that observed in 1996. The headwaters of a Semois river, described in previous studies as polytrophic and devoid of vegetation, show a slight improvement, with the appearance of macrophytic species. In some parts of the Chiers sub-basin, however, resistant species observed in 1999 persist.
The area-angular momentum inequality for black holes in cosmological spacetimes

For a stable, marginally outer trapped surface (MOTS) in an axially symmetric spacetime with cosmological constant Λ > 0 ? > and with matter satisfying the dominant energy condition, we prove that the area A and the angular momentum J satisfy the inequality 8 &pgr; ∣ J ∣ ≤ A ( 1 − Λ A / 4 &pgr; ) ( 1 − Λ A / 12 &pgr; ) ? > , which is saturated precisely for the extreme Kerr–de Sitter family of metrics. This result entails a universal upper bound ∣ J ∣ ≤ J max ≈ 0.17 / Λ ? > for such MOTS, which is saturated for one particular extreme configuration. Our result sharpens the inequality 8 &pgr; ∣ J ∣ ≤ A ? > (Dain and Reiris 2011 Phys. Rev. Lett. 107 051101, Jaramillo, Reiris and Dain 2011 Phys. Rev. Lett. D 84 121503), and we follow the overall strategy of its proof in the sense that we first estimate the area from below in terms of the energy corresponding to a ‘mass functional’, which is basically a suitably regularized harmonic map S 2 → H 2 . > However, in the cosmological case this mass functional acquires an additional potential term which itself depends on the area. To estimate the corresponding energy in terms of the angular momentum and the cosmological constant we use a subtle scaling argument, a generalized ‘Carter-identity’, and various techniques from variational calculus, including the mountain pass theorem.
Title Catalytic analysis of APOBEC 3 G involving real-time NMR spectroscopy reveals nucleic acid determinants for deamination

APOBEC3G (A3G) is a single-stranded DNA-specific cytidine deaminase that preferentially converts cytidine to uridine at the third position of triplet cytosine (CCC) hotspots. A3G restricts the infectivity of viruses, such as HIV-1, by targeting CCC hotspots scattered through minus DNA strands, reverse-transcribed from genomic RNA. Previously, we developed a real-time NMRmethod and elucidated the origin of the 3'! 5' polarity of deamination of DNA by the C-terminal domain of A3G (CD2), which is a phenomenon by which a hotspot located closer to the 5'-end is deaminated more effectively than one less close to the 5'-end, through quantitative analysis involving nonspecific binding to and sliding along DNA. In the present study we applied the real-time NMRmethod to analyze the catalytic activity of CD2 toward DNA oligonucleotides containing a nucleotide analog at a single or multiple positions. Analyses revealed the importance of the sugar and basemoieties throughout the consecutive 5 nucleotides, the CCC hotspot being positioned at the center. It was also shown that the sugar or basemoieties of the nucleotides outside this 5 nucleotide recognition sequence are also relevant as to CD2's activity. Analyses involving DNA oligonucleotides having two CCC hotspots linked by a long sequence of either deoxyribonucleotides, ribonucleotides or abasic deoxyribonucleotides suggested that the phosphate backbone is required for CD2 to slide along the DNA strand and to exert the 3'! 5' polarity. Examination of the effects of different salt concentrations on the 3'! 5' polarity indicated that the higher the salt concentration, the less prominent the 3'! 5' polarity. This is most likely the result of alleviation of sliding due to a decrease in the affinity of CD2 with the phosphate backbone at high salt concentrations. We also investigated the reactivity of substrates containing 5-methylcytidine (5mC) or 5hydroxymethylcytidine, and found that A3G exhibited low activity toward 5mC.