Miracle-dz/UniFAR-FTUs huggingface dataset:
EXTRACTING INNOVATIVE BUYERS BY SCORING USING INNOVATOR THEORY

For companies that want to sell a high volume of products, it is important to identify innovative buyers to help with product marketing efforts. The purpose of this paper is to construct a model extracting whether users are innovative buyers or not from their purchase histories at physical stores and from access logs from an online-to-offline (O2O) site. Innovative buyers are users who influence other users' product purchases, also known in innovator theory as innovators and early adopters. They purchase products quickly, visiting physical stores such as supermarkets and convenience stores. In other words, innovative buyers are known to have high cosmopolite natures. In extracting innovative buyers, we estimated the speed of user product purchases and their cosmopolite natures. This estimation index can also be referred to as innovator scores. We went on to verify this method with socioeconomic status points, personality points and communication points (SPC points), using consciousness data and profile data collected from a panel on an O2O site. Thus, we showed that innovative buyers could be extracted using this new method, and the accuracy was higher than that of traditional methods measuring only the speed from product sale start to user purchase.
Denitrification and associated nitrous oxide and carbon dioxide emissions from the Amazonian wetlands

Abstract. In this paper, we quantify the CO2 and N2O emissions from denitrification over the Amazonian wetlands. The study concerns the entire Amazonian wetland ecosystem with a specific focus on three floodplain (FP) locations: the Branco FP, the Madeira FP and the FP alongside the Amazon River. We adapted a simple denitrification model to the case of tropical wetlands and forced it by open water surface extent products from the Soil Moisture and Ocean Salinity (SMOS) satellite. A priori model parameters were provided by in situ observations and gauging stations from the HYBAM Observatory. Our results show that the denitrification and the trace gas emissions present a strong cyclic pattern linked to the inundation processes that can be divided into three distinct phases: activation, stabilization and deactivation. We quantify the average yearly denitrification and associated emissions of CO2 and N2O over the entire watershed at 17.8 kgN ha −1 yr −1 , 0.37 gC- CO2 m −2 yr −1 and 0.18 gN- N2O m −2 yr −1 respectively for the period 2011–2015. When compared to local observations, it was found that the CO2 emissions accounted for 0.01 % of the integrated ecosystem, which emphasizes the fact that minor changes to the land cover may induce strong impacts on the Amazonian carbon budget. Our results are consistent with the state of the art of global nitrogen models with a positive bias of 28 %. When compared to other wetlands in different pedoclimatic environments we found that the Amazonian wetlands have similar emissions of N2O with the Congo tropical wetlands and lower emissions than the temperate and tropical anthropogenic wetlands of the Garonne (France), the Rhine (Europe) and south-eastern Asia rice paddies. In summary our paper shows that a data-model-based approach can be successfully applied to quantify N2O and CO2 fluxes associated with denitrification over the Amazon basin. In the future, the use of higher-resolution remote sensing products from sensor fusion or new sensors like the Surface Water and Ocean Topography (SWOT) mission will permit the transposition of the approach to other large-scale watersheds in tropical environments.
Behavior of binned kernel density estimators for non-stationary random fields

We investigate the asymptotic behavior of binned kernel density estimators for dependent and locally non-stationary random fields converging to stationary random fields. We focus on the study of the bias and the asymptotic normality of the estimators. A simulation experiment conducted shows that both the kernel density estimator and the binned kernel density estimator have the same behavior and estimate accurately the true density when the number of fields increases.
Basic Physics Model for PdH Thermodynamics

We made use of the very powerful modern density functional code Quantum Espresso for an ab initio calculation of the thermodynamics of PdH. There is an energy offset of about 100 meV in the calculation as compared to experiment. We developed an empirical correction to the theoretical energies which allowed us to fit the experimental results for excess enthalpy and excess entropy with some success. We also explored a new model that posits a weak binding between absorbed hydrogen atoms in PdH, and is able to provide a better fit to the experimental results. The model provides a simple physical explanation of observed phase diagram, and also of the observed dependence of the thermodynamic variables (excess enthalpy and excess entropy) on loading. © 2014 ISCMNS. All rights reserved. ISSN 2227-3123
THE ROLE OF EMOTIONAL ACCEPTANCE AND AWARENESS IN THE RELATIONSHIP BETWEEN POSTTRAUMATIC STRESS DISORDER SYMPTOMS AND POSTTRAUMATIC GROWTH AMONG SURVIVORS OF SEXUAL ASSAULT

iii ACKNOWLEDGEMENTS . v CHAPTER ONE: REVIEW OF THE LITERATURE . 1 Sexual Victimization . 1 Posttraumatic Growth . 5 Experiential Avoidance . 15 Role of Mindfulness . 20 CHAPTER TWO: THE PRESENT STUDY . 25 Present Study . 25 Method . 26 Participants . 26 Measures . 26 Design and Analysis . 29 Procedure . 29 Data Analytic Strategy . 30 CHAPTER THREE: RESULTS . 32 Correlational Analyses . 32 Moderation Analysis . 32 CHAPTER FOUR: DISCUSSION . 34 Summary of Findings . 34 APPENDIX A: INFORMED CONSENT . 40 APPENDIX B: TABLES . 43
NIR analysis as an integrated tool for the assessment of soil quality

Near infrared reflectance spectroscopy (NIRS) is a rapid, non-destructive, reproducible and cost-effective analytical method involving diffuse reflectance measurement in the near infrared region (NIR; 780-2500 nm). A NIR sensing device able to collect in-situ 3D spectral data through an entire soil profile, allowing a rapid and objective soil classification. A set of regression methods allows the prediction of many properties of unknown samples using calibration equations that relate spectral information to sample properties measured by conventional methods, within a calibration subset. soil quality (pH, organic C, total N, P, Ca, Mg, K, CEC, clay, silt) and developed ordinal soil condition classes (poor, average, good), which were used to identify spectral wavebands that could diagnose soil condition. They found that five wavelengths were related to their soil quality index: relative reflectance at 570, 1410, 2040 and 2390 nm were negatively correlated with soil condition class whereas relative reflectance at 1940 nm (which is almost certainly due to O–H bond of water) was positively correlated with soil condition class.
HOW CAN SMALL-SCALE EVENTS CONTRIBUTE TO THE TOURISM PROGRESS OF AN UNDEVELOPED BORDER AREA? LESSON FROM EASTERN SERBIA

The study analyses the role and impact of the event "Danube Fair" (Eastern Serbia) in the tourism progress of the less-advantaged border area. The aim of this paper is to emphasize the significance of the role of events in the tourism development of rural settings and the attraction of domestic and foreign visitors with the specific offer of this part of Serbia. The tourism valorization of the event “Danube Fair” was done according to the geographical and economic criteria, and it was determined that it belongs to the category of regional events. In the paper, special attention was paid to the analysis of tourists’ satisfaction with this event as a geographical criterion, especially in dependence with the visitors’ origin, since it is what the positioning of an event on the tourism market depends on, both on the local and on the international level. “Danube Fair” presents the example of a relatively small-scale event, which can become a basis of tourism growth, with proper valorization and affirmation of local products and services, and their introduction to the international tourism market.
Monitoring of phase jitter in fibre optic time and frequency transfer systems

The phase jitter enables to assess quality of signals transmitted in a bi-directional, long-distance fibre optic link dedicated for dissemination of the time and frequency signals. In the paper, we are considering measurements of jitter using a phase detector the detected frequency signal and the reference signal are supplied to. To cover the wideband jitter spectrum the detected signal frequency is divided and – because of the aliasing process – higher spectral components are shifted down. We are also examining the influence of a residual jitter that occurs in the reference signal generated by filtering the jitter occurring in the same signal, whose phase fluctuations we intend to measure. Then, we are discussing the evaluation results, which were obtained by using the target fibre optic time and frequency transfer system.
Taking Advantage of Data Dimensionality Reduction for Dynamic Gesture Recognition from Incomplete Data

—Continuous gesture spotting is a major topic in human-robot interaction research. Human gestures are captured by sensors that provide large amounts of data that can be redundant or incomplete, correlated or uncorrelated. Data dimensionality reduction (DDR) techniques allow to represent such data in a low-dimensional space, making the classiﬁcation process more efﬁcient. This study demonstrates that DDR can improve the classiﬁcation accuracy and allows the classiﬁcation patterns gesture patterns with incomplete data, i.e., with the initial 25%, 50% or 75% of data representing a given dynamic gesture (DG) - time series of positional and hand shape data. Re-sampling with bicubic interpolation and principal component analysis (PCA) were used as DDR methods. Experimental tests indicate that after DDR PCA the classiﬁcation accuracy is higher with 50% of gesture data (100% accuracy) than with 100% of gesture data (96% accuracy), tested in a library of 10 hand/arm dynamic gestures. Recognized gestures are used to control a robot in an collaborative process.
A novel monoterpenoid from leaf exudates of Tarchonanthus camphoratus with anti-leishmanial activities

One new monoterpenoid; 2,3,3a,4,5,7a-hexahydro-7a-methyl-4-methylene-1H-inden-5-ol (1) along with five known compounds including; two sesquiterpenes, costic acid derivative (2), (-)-parthenolide (3), and four known methoxylated flavonoids; 5,7,3´,4´-tetrahydroxy-3-methoxyflavone (4), 5,7,4´-trihydroxy-6-methoxyflavone (5), 5,7,3´,4´-tetrahydroxy-6-methoxyflavone (6) and 5-hydroxy-7,8-dimethoxyflavanone (7) were characterized from the leaf exudates of Tarchonanthus camphoratus. The structures of these compounds were determined based on spectroscopic data analyses. The new compound exhibited good anti-fungal activity against Cryptococcus neoformans with an IC50 value of 5.62 µg/mL. Compounds 1 and 2 exhibited moderate anti-leishmanial activities against Leishmania donovani with IC50 values of 14.17 and 12.84 µg/mL, respectively, (vs 0.85 for pentamidine and 0.12 µg/mL for amphotericin B). Compound 6 and 7 also showed anti-leishmanial activities with IC50 values of 26.24 and 23.15 µg/mL, respectively. All compounds tested were not cytotoxic at 5 µg/mL.
The prevalence of depression, anxiety, and stress in patients with breast cancer in Southeast Iran in 2019: a cross-sectional study

Introduction. Today, breast cancer patients suffer from various psychological symptoms that impose irreversible effects on their quality of life. The aim of the present study was to determine the prevalence of depression, anxiety, and stress in patients with breast cancer. Material and methods. This descriptive study was performed on 190 women with breast cancer from January 1, 2019 to July 30, 2019. Data collection was carried out using a convenience sampling method. The Standard Depression, Anxiety, and Stress Scale (DASS-21) was used to assess depression, anxiety, and stress. Results. The mean age of the patients was 46.3 years. Results showed the prevalence of depression, anxiety, and stress to be 28.4%, 43.2%, and 14.7%, respectively. Conclusion. The results indicate that it is vital to measure the level of depression and anxiety in women with breast cancer, which are two common mental disorders in breast cancer.
Co-deposition of Palladium and other Transition Metals in H2O and D2O Solutions

The co-deposition of palladium, ruthenium, rhenium, nickel, and iridium were investigated in H2O and D2O ammonia systems (NH4Cl/NH3). Significant amounts of excess power were observed only in the deuterated Pd/D2O system. There was no anomalous excess power observed for the co-deposition of ruthenium, rhenium or nickel in any H2O or D2O experiment. © 2014 ISCMNS. All rights reserved. ISSN 2227-3123
Multilevel modelling of tabular counts via Deconstructed Maximum Likelihood

A multilevel Poisson regression model is proposed for modelling spatially clustered tabular count data. Counts in tabular data are usually perturbed to reduce disclosure risk, and this can have great impact on small cell counts. We propose a Poisson mixed model with multiplicative random effect that is Gamma distributed. Compared to the existing model using log-Normal random effect, our model utilizes summary statistics of individual with events, which has benefits in terms of privacy protection, data quality improvement and small area analysis, since perturbation of event counts is no longer required. The parameters in our model are estimated via Deconstructed Maximum Likelihood, a new framework where dataset of fundamental different nature, namely individual and area level variables in our context, are used for inference without having to combine them into a single analysis file. The modularity approach is representative of the structure of multilevel data and is scalable as it avoids the repetition of area level variables over individuals living in the common geographical region. Also, our model has closed form likelihood, rendering inference exact without the use of numerical integration. When the variance of the log-Normal random effect is small, the two models produce similar estimates. We apply our model to the Australian unemployment data and show the potential benefits of incorporating it into data extraction tools.
On the expected weight of the theta graph on uncertain points

Given a point set $Ssubset mathbb{R}^d$, the $theta$-graph of $S$ is as follows: for each point $sin S$, draw cones with apex at $s$ and angle $theta$ %fix a line through $p$ at each cone and connect $s$ to the point in each cone such that the projection of the point on the bisector of the cone is the closest to~$s$. One can define the $theta$- graph on an uncertain point set, i.e. a point set where each point $s_i$ exists with an independent probability $pi_i in (0,1]$. In this paper, we propose an algorithm that computes the expected weight of the $theta$-graph on a given uncertain point set. The proposed algorithm takes $O(n^2alpha(n^2,n)^{2d})$ time and $O(n^2)$ space, where $n$ is the number of points, $d$ and $theta$ are constants, and $alpha$ is the inverse of the Ackermann's function.
The Interplay of Reason and Emotion

Understanding society requires incorporating all of its human dimensions, including both reason and emotion. Emotion has been underplayed because it complicates the study of society, making it difficult to build mathematical models, the emblem of a true science, at least as it has been interpreted over the past several hundred years. Humanistic portrayals of society have thus been marginalized, as also is art, generally, because they undermine rationality, the hallmark of authority in a science-based world. However, science, itself has come to be seen as somewhat emotional, as scholars examine its history, to say nothing of human beings, generally, who are emotional through and through. Other versions of scientific and scholarly research are explored to uncover these dimensions and their interconnections, including recent developments in biology, which focus on the possible importance of consciousness in nature.
Analysis of Machine Learning for Processing Big Data in High Performance Computing: A Review

In the present situation, it is worthy for all that the computerized information for example big data is quickly growing in all requests and turning out to be difficulties in convenience forms. Age of the valuable data from the multiplied information is a fascinating procedure might be called as preparing of the information. Presently a day's prepared informational collections have an imperative situation in discovering information through machine learning. The Authors may need to discover new thoughts of machine learning or profound learning methods for machine in the field of preparing information for superior figuring. This paper speaks to the review of different machine procedures or strategies applied before train data sets for information extraction of information in enormous information investigation to improve performance computing like cloud computing or grid computing. This paper could be as starting point and base of examination and has a key an incentive in the field of machine learning.
Applications and extensions of Granger Causality in Neuroimaging: inferences on information flow over the brain.

Vector autoregression (VAR) is one of the most used models to make inferences about relations between time series. Parameters estimation is simple and the interpretation of statistical results is intuitive. In the analysis of fMRI data, Granger causality methods based on VAR estimation have been used to study effective and functional connectivity between brain regions. However, stationarity condition is usually not valid in many real data applications. Furthermore, the BOLD signal measured in fMRI can be influenced by artefacts that may generate spurious correlation between the signals. Finally, the high dimensionality of the data is an obstacle to infer relationships between neural modules. In this paper, we present a review of some approaches developed to tackle these problems. We describe the Dynamic Vector Autoregressive Model based on Wavelets expansion, Partial Directed Coherence Analysis of fMRI data and the Cluster Granger Analysis.
Application of nonlinear mixed models to describe growth patterns and sexual dimorphism in capybaras ( Hydrochoerus hydrochaeris )

In this paper a von Bertalanffy functions was fitted to describe long-term changes in body mass of capybaras (Hydrochoerus hydrochaerys). A multilevel nonlinear mixed effects model was employed because it allowed for estimation of all growth profiles simultaneously, and different sources of variation (i.e., sex, individuals, and litter effects) were incorporated directly into the parameters. Furthermore, variance in-homogeneity and within-individuals correlation were introduced to the functions. Inclusion of a variance of power function and a continuous autoregressive process of first order rendered a substantially improved fit to data. The sample consisted of 673 weight data obtained from 33 females and 37 males. Significant variance components were recorded for both individuals and litters. Also significant differences were observed in growth patterns between sexes. Males showed asymptotic weights and age at the inflection point significantly higher than females, while the constant growth rate was lower. These differences could be interpreted in terms of mating system of this species since males have chances to mate only when they reach a large enough size to compete with other males. For females, however, the possibility of mating does not depend on aggressive confrontations, so if they reach sexual maturity earlier, they may extend the period when they are sexually active.
Abundance and genetic variation of the coral-killing cyanobacteriosponge Terpios hoshinota in the Spermonde Archipelago, SW Sulawesi, Indonesia

The cyanobacteriosponge Terpios hoshinota is expanding its range across the Indo-Pacific. This species can have massive outbreaks on coral reefs, actively kill corals it overgrows and affect the entire benthic community. Although it has received much attention on the post-outbreak follow-up, little is known about its ecology, habitat preferences, and the possible environmental triggers that cause its outbreaks. We present a baseline study in the Spermonde Archipelago, Indonesia, where T. hoshinota was first observed in 2012. We surveyed 27 reefs and recorded patches between 14 and 217 cm2, at four reef sites (~15% cover). The sponge was found on both mid-shelf and outer shelf reefs but not close to the coast and the city of Makassar. Differences in benthic community structure, as well as spatial variables relating to the on-to-offshore gradient in the Spermonde archipelago, neither constrained nor promoted its expansion. Patches of the sponge were mostly overgrowing branching corals, belonging to Acroporidae species. Genetic variation within T. hoshinota was studied by sequencing partitions of the mitochondrial CO1 and nuclear ribosomal 28S gene. Two haplotypes were found within the Spermonde archipelago, which differed from the CO1 sequence in GenBank. The present study provides an indication of habitat preferences of T. hoshinota in non-outbreak conditions, although it is still unclear which environmental conditions may lead to the onset of its outbreaks.
Evaluation of in vitro antibacterial activity and phytochemical profile of aqueous leaf extract of Asystasia variabilis

This study evaluated the in vitro antibacterial effect and the phytochemical profile of aqueous extract of fresh mature leaves of Asystasia variabilis, a Sri Lankan indigenous plant, against four common wound infective bacteria (Staphylococcus aureus, Bacillus subtilis, Pseudomonas aeruginosa and Escherichia coli) using Kirby-Bauer disk diffusion test. Gentamicin 10 μg/ disk and distilled water was used as positive and negative controls respectively The study revealed, for the first time, that the extract possessed significant antibacterial activity against all four test organisms in a concentration dependent manner (r values ranging from 0.921-0.992, P<0.01) with inhibition zone diameters ranging between 8 and 28 mm. Highest antibacterial activity was exhibited against B. subtilis at 1000 μg/ disk (27.43±0.02 mm). The extract showed inhibitory effects comparable to gentamicin towards B. subtilis and P. aeruginosa at 500 μg/ disk and towards E. coli and S. aureus at 1000 μg/ disk. Qualitative phytochemical screening revealed the presence of flavonoids, tannins, phenols, cardiac glycosides, amino acids, carbohydrates, alkaloids and saponins. Therefore it is likely that the antibacterial effect of the extract is mediated by synergistic mechanisms. Furthermore, results of this study scientifically justified the claim traditional and folk medicine in the treatment of abscesses, wounds and ulcers and indicated the potential for the development of a novel drug from mature leaves of Asystasia variabilis.