Miracle-dz/UniFAR-FTUs huggingface dataset:
Temporal coherence of aboveground net primary productivity in mesic grasslands

Synchrony in ecological variables over wide geographic areas suggests that large-scale environmental factors drive the structure and function of ecosystems and override more local-scale environmental variation. Described also as coherence, this phenomenon has been documented broadly in the ecological literature and has recently received increasing attention as scientists attempt to quantify the impacts of global changes on organisms and their habitats. Using a mesic grassland site in North America, we assessed coherence in ecosystem function by quantifying similarity in aboveground net primary production (ANPP) dynamics in 48 permanent sampling locations (PSLs) over a 16-yr period. Our primary objective was to characterize coherence across a broad geographic region (with similar ecosystem structure and function), and we hypothesized that precipitation and a similar fire frequency would strengthen coherence between PSLs. All 48 PSLs at our site (Konza Prairie Biological Station, Manhattan, KS, USA; KPBS) were exposed to a similar regional driver of ANPP (precipitation); however, local drivers (including differences in fire frequency and soil depth at different topographic positions) varied strongly among individual PSLs. For the purpose of this assessment, the watershed-level experimental design of KPBS was considered a model, which represented different fire management strategies across the Great Plains Region. Our analyses revealed a site-level (KPBS) coherence in ANPP dynamics of 0.53 for the period of 1984-1999. Annual fire enhanced coherence among PSLs to 0.76, whereas less frequent fire (fire exclusion or a 4-yr fire return interval) failed to further increase coherence beyond that of the KPBS site level. Soil depth also strongly influenced coherence among PSLs with shallow soils at upland sites showing strong coherence across fire regimes and annually burned uplands closely linked to annual precipitation dynamics. The lack of coherence in ecosystem function in PSLs with deep soils and low fire frequencies suggests that conservation and management efforts will need to be more location specific in such areas where biotic interactions may be more important than regional abiotic drivers.
Modifying Moral Dilemma Judgments: How the priming of Moral Rules Modifies Responses to Moral Dilemmas

Prior decision making research has covered issues ranging from economic decisions to moral judgment. Moral judgment studies indicate that the use of hypothetical dilemmas presents the ability to explore decision processes regarding morality (Greene, Sommerville, Nystrom, Darley, & Cohen, 2001; Hauser, 2006; Koenigs et aI., 2007). Largely, judgment is measured by asking respondents to comment on the permissibility of an action presented in the moral dilemma (Ham & Van den Bos, 2010; Hauser, Cushman, Young, Jin, & Mikhail, 2007; O'Neill & Petrinovich, 1998). A variety ofexplanations have been used to try and explain moral judgment, but rarely have attempts been made to modify judgment. The present study examines the possibility of using priming to alter moral judgment. Prior results indicate that conceptual priming is sufficient in the modification ofmoral judgment (Broeders et aI., 2011). The results of the experiment indicate that priming may not clearly alter permissibility beliefs in moral judgment. Overall, decision makers did not find the moral dilemma permissible, which may have countered any priming effects.
Low Rank Tensor Completion via Partial Sum Minimization of Singular Values

In this paper, we investigate the low-rank tensor completion problem, in which we wish to estimate missing values of tensors from incomplete samples its entries. In real world, the low-rank tensor can be seen everywhere and the exact rank of it is often known. Based on the fact that singular values before the target rank does not affect rank minimization of tensors, we propose low rank tensor completion via partial sum minimization of singular values algorithm(PSSV-LRTC). Some experiments are performed on both synthetic data and real applications; all results show that our algorithm has a higher precision and convergence rate than previous work. Keywords—tensor completion; matrix completion; nuclear norm minimization; alternating direction method of multipliers
Comparison of Single and Ensemble Classifiers of Support Vector Machine and Classification Tree

An ensemble consists of a set of individually trained classifiers (such as Support Vector Machine and Classification Tree) whose predictions are combined by an algorithm. Ensemble methods is expected to improve the predictive performance of classifier. This research aims to assess and compare performance of single and ensemble classifiers of Support Vector Machine (SVM) and Classification Tree (CT) by using simulation data. The simulation data is based on three data structures which are linearly separable, linearly nonseparable and nonlinearly separable data. The simulation data results show that SVM has the ability to classify the data better than CT. Ensemble method improve classification performance and more stable than single classifier. This was due to ensemble SVM has the smallest percentage of the average misclassification rate and standard deviation.
Modified Einstein's gravity to probe the sub- and super-Chandrasekhar limiting mass white dwarfs: a new perspective to unify under- and over-luminous type Ia supernovae

Type Ia supernovae (SNeIa), used as one of the standard candles in astrophysics, are believed to form when the mass of the white dwarf approaches Chandrasekhar mass limit. However, observations in last few decades detected some peculiar SNeIa, which are predicted to be originating from white dwarfs of mass much less than the Chandrasekhar mass limit or much higher than it. Although the unification of these two sub-classes of SNeIa was attempted earlier by our group, in this work, we, for the first time, explain this phenomenon in terms of just one property of the white dwarf which is its central density. Thereby we do not vary the fundamental parameters of the underlying gravity model in the contrary to the earlier attempt. We effectively consider higher order corrections to the Starobinsky-f(R) gravity model to reveal the unification. We show that the limiting mass of a white dwarf is ∼ M⊙ for central density ρc ∼ 1.4×108 g/cc, while it is ∼ 2.8M⊙ for ρc ∼1.6× 1010 g/cc under the same model parameters. We further confirm that these models are viable with respect to the solar system test. This perhaps enlightens very strongly the long standing puzzle lying with the predicted variation of progenitor mass in SNeIa.
Damage Monitoring in Composite Stiffened Skin Using Fiber Bragg Grating under Tensile and Three-Point Loading

One of the problems associated with loading a fully composite structure with joints is that the loads are not linear through the neutral axis of the structure but are collinear; this induces additional moment and creates a load in the normal direction, which is typically a critical load because it can create delamination and can only be withstood if it is small. Another problem is that the structure is difficult to inspect using conventional methods because of limited accessibility. With fiber Bragg grating (FBG), the problem can potentially be solved in structures with a stiffness mismatch. The model used to represent the problem above is a composite stiffened skin with two loading cases: tensile and three-point bending. Additionally, FBG is used to monitor and characterize the delamination caused by both loading cases. Finite element modeling (FEM) with traction versus separation theory is performed to determine the critical area on the specimen for placement of the FBG before manufacturing and testing. In this research, FBG can successfully monitor and characterize delamination caused by both loading cases in structures that have mismatched stiffnesses. Also, FBG can predict the delamination growth quantitatively. A spectrum graph of the FBG results can be used to replace a conventional mechanical graph for use in structural health monitoring.
Statistical Methods for Detecting Spatial Configuration Errors in Traffic Surveillance Sensors

With large-scale deployment of traffic surveillance sensors becoming commonplace, it becomes critical to maintain correct information about the spatial configuration of the sensors. The problem is burdensome when hundreds or thousands of sensors are deployed. One common configuration error is the switching of directions of highway loop detectors that share the same cabinet. Proposed are semiautomatic and automatic methods for detecting such errors, on the basis of the strong correlations between measurements made by spatially close sensors. The semiautomatic method uses a multidimensional scaling (MDS) map of sensors, which visually displays the similarity between sensor measurements and enables one to easily identify sensor mislabeling. The automatic method uses a scoring scheme that computes the probability of sensor mislabeling from the pairwise distance or similarity matrix. The algorithm, tested on data from a four-lane freeway consisting of 64 sensor locations—10 of which had switched locations—successfully detected all errors with 5.6% false detection rate, even with poor data quality. The MDS map can be used for other applications, such as detection of sensor malfunctions.
Mechanical characterization of visually classified, small-diameter laricio pine round timber.

This work reports the mechanical characterization of small-diameter logs (diameter < 23 cm) of laricio pine (Pinus nigra subsp. Saltzmannii) from the forests of Cuenca, Spain. Some 445 machine-rounded small logs with final diameters of 8, 10, 12 and 14 cm were examined in accordance with the standard EN 14251, and the conclusion reached that ungraded material can be assigned a strength class of C14 (according to EN 338), while graded material can be assigned to different combinations of strength classes (C18/C14 or C20/C16) depending on the visual grading standard used. Grading yields were examined using three different standards, DIN 4074, VPS-SRT-2 and prEN 14544, and the results obtained with each compared to those provided by the optimal ranking methodology set out in standard EN 14081-2. VPS-SRT-2 appeared to be the most suitable visual grading standard. There is a need to design mixed grading methods based on the use of non-destructive tests and the visual evaluation of specific wood features.
SERS Application for Analysis of Live Single Cell

Monitoring changes of the protein contents and other macromolecules inside a living single cell during the key cellular processes such as cell differentiation, division, and apoptosis is a challenge for researchers. Raman spectroscopy is a powerful analytical technique for several biomedical applications that is rapid, reagent-free, and non-destruc-tive while limited application with its weak signal. Surface-enhanced Raman scattering (SERS) technique is widely used to enhance the Raman signal (10 9-15 fold) by using surface Plasmon resonance of noble metal nanostructures (e.g. silver, gold, copper). SERS is a non-destructive spectroscopic method applied for biomedical samples. In this chapter, we will discuss the principles and fundamentals of SERS technique, theories and dif- ferent strategies to obtain SERS signals such as immobilization of metal colloids on a substrate. Also, we show the SERS applications including the identification and discrimi- nation of different types of cells (healthy and nonhealthy cells, e.g., cancer cells), and the interaction of cells with different drugs will also be discussed on monolayer bulk cells as well as on single-cell basis and for stem cell differentiation. In addition, we show the coupling of SERS with electrochemical techniques (EC-SERS) as spectroelectrochemical technique and its applications in biology, bioanalytical, and life science.
Assessment of PISA 2012 Results With Quantile Regression Analysis Within The Context of Inequality In Educational Opportunity

The importance of educational opportunity inequality has been increasing within the context of education systems during recent years. In addition to quality in education, opportunity equality is among the significant paradigms in countries of high educational performance. Thus, it is of utmost importance to research the relationship between socio-economic characteristics of the students and achievement based on opportunity equality. Especially to remove the gap observed in Turkish literature is among the objectives of the present study. The main objective of the study is to assess the socio-demographic characteristics that affect the achievement of students in mathematics within the context of educational opportunity equality for PISA 2012 Turkey sample. Data analysis was conducted with quantile regression (QR) and classical linear regression (OLS). As a result, it was determined that students’ family background, familiarity with information and communication technology and school climate were affective on mathematics achievement. It was observed that as parentel education, educational resources at home, and index of familty wealth increased, mathematics achievement increased as well. It was also observed that time of computer use had a negative effect on achievement in mathematics. Furthermore, study findings identified that the achievement of male students was higher than females.
First-Person Shooters: Immersion and Attention

First-Person Shooter computer games are designed to be immersive experiences yet the phenomenon of immersion is little understood. This article surveys theories of immersion in virtual worlds and examines FPS game elements that might contribute to the state. The roles of attention and positive feedback in facilitating player immersion in FPS games is explored. In particular, the role of selective attention is highlighted before the article finishes with a discussion on the design of immersion in FPS games using the principles presented here.
Time reversal of waves in a weakly perturbed random medium

Abstract. In a time reversal experiment, a signal recorded by an array of transducers and sent back time-reversed into the same medium approximately refocuses on the original source center. The refocusing resolution is improved in an inhomogeneous medium. In this work we study the effect of changes in the medium, namely, the case when back propagation takes place in a perturbed medium. Under the paraxial approximation assumption for a medium with weak inhomogeneities we consider a high frequency white noise regime. We show that relatively small perturbations do not affect the stable refocusing (self-averaging) for a localized source, but produce an interesting blurring of the refocused time signal. In some simple situations this effect can be explicitly quantified and related to the statistical model for the medium.
A major gene for litter size in pigs.

Identification of individual genes or anonymous genetic markers associated w ith litter size in the pig could have a great economic impact on the swine industry. Such genetic polymorphisms could be employed in marker-assisted selection programs to improve reproductive efficiency. We report the discovery of a major gene for litter size, associated with polymorphism of the estrogen receptor gene, which has a significant effect on total number born and number born alive in the pig. Differences among genotypes of the estrogen receptor gene account for a significant increase in litter size of approximately 1.5 pigs per litter born and over 1 pig born alive. Results also suggest that the favorable gene has no negative pleiotropic effects on growth rate and backfat. Use of this gene in marker-assisted selection / introgression programs is expected to be extremely important, particularily in the development of synthetic lines of pigs involving the Meishan breed. The results provide a successful example of the candidate gene approach and portend the possible benefits of future molecular genetic research in pigs.
Phialophora Melinii (NFCCI 3617): A Newly Isolated Psychrotolerant Fungus That Produces Enhanced Laccase Under the Influence of Organic Solvents

A psychrotolerant fungus, isolated from decomposing pine needle debris, is investigated for laccase production under the influence of 5 organic solvents. The fungus was identified as Phialophora melinii and was able to grow between 4 to 35 °C (opt. 25 °C) and 2-14 pH (opt. 5-7). In quantitative estimations that were carried out at optimum growth temperature and pH, the fungal laccase was estimated to be 21.0 ± 4.0 U/L. Native PAGE study revealed 35 kDa molecular mass of the fungal laccase. Supplementation of organic solvents namely, methanol, ethanol, acetone, n-propanol and iso-propanol in varying concentrations (0.5%-2.0%, separately), significantly affected the production of fungal laccase. Out of 5 solvents used, n-propanol was found to be the most efficient enhancer of laccase production. n-Propanol (0.5%) resulted in maximum enhancement (7 folds) in laccase production at 18th day of incubation. Methanol, iso-propanol and ethanol were able to enhance laccase production up to 5-6 folds in comparison to control with respect to the varying concentration and incubation length. Age of the fungal culture (incubation days) was observed as an important factor for laccase production. Use of low molecular compounds in enhancing the fungal laccase production may be considered as an eco-friendly approach.
SELECTING PROFILES OF IN DEBT CLIENTS OF A BRAZILIAN TELEPHONE COMPANY: NEW LASSO AND ADAPTIVE LASSO ALGORITHMS IN THE COX MODEL

Variable selection plays an important rule in identifying possible factors that could predict the behavior of clients with respect to the bill payments. The Cox model is the standard approach for modeling the time until starting the lack of payments. Parsimony and capacity of predicting are some desirable characteristics of statistical models. This paper aims at proposing a new forward stagewise Lasso (least absolute shrinkage and selection operator) algorithm and applying it for variable selection in the Cox model. The algorithm can be easily extended to run the Adaptive Lasso (ALasso) approach.
HERschel Observations of Edge-on Spirals (HEROES) III. Dust energy balance study of IC 2531

We investigate the dust energy balance for the edge-on galaxy IC 2531, one of the seven galaxies in the HEROES sample. We perform a state-of-the-art radiative transfer modelling based, for the first time, on a set of optical and near-infrared galaxy images. We show that by taking into account near-infrared imaging in the modelling significantly improves the constraints on the retrieved parameters of the dust content. We confirm the result from previous studies that including a young stellar population in the modelling is important to explain the observed stellar energy distribution. However, the discrepancy between the observed and modelled thermal emission at far-infrared wavelengths, the so-called dust energy balance problem, is still present: the model underestimates the observed fluxes by a factor of about two. We compare two different dust models, and find that dust parameters, and thus the spectral energy distribution in the infrared domain, are sensitive to the adopted dust model. In general, the THEMIS model reproduces the observed emission in the infrared wavelength domain better than the popular BARE-GR-S model. Our study of IC 2531 is a pilot case for detailed and uniform radiative transfer modelling of the entire HEROES sample, which will shed more light on the strength and origins of the dust energy balance problem.
Compression-based inference on graph data

We investigate the use of compression-based learning on graph data. General purpose compressors operate on bitstrings or other sequential representations. A single graph can be represented sequentially in many ways, which may inuence the performance of sequential compressors. Using Normalized Compression Distance (NCD), we test a sequential compressor versus a native graph compressor. We use both synthetic, randomly generated graphs and reallife datasets. We conclude that, even under adverse circumstances, sequential representations contain enough structure for shallow algorithms to perform inference successfully. Algorithms that operate directly on the graph representation usually require a considerable increase in resources, but do allow for an increase in performance also.
A Gradient Based Iterative Solutions for Sylvester Tensor Equations

This paper is concerned with the numerical solution of the Sylvester tensor equation, which includes the Sylvester matrix equation as special case. By applying hierarchical identification principle proposed by Ding and Chen, 2005, and by using tensor arithmetic concepts, an iterative algorithm and its modification are established to solve the Sylvester tensor equation. Convergence analysis indicates that the iterative solutions always converge to the exact solution for arbitrary initial value. Finally, some examples are provided to show that the proposed algorithms are effective.
Automated Data Collection for Determining Statistical Distributions of Module Power Undergoing Potential-Induced Degradation

We propose a method for increasing the frequency of data collection and reducing the time and cost of accelerated lifetime testing of photovoltaic modules undergoing potential-induced degradation (PID). This consists of in-situ measurements of dark current-voltage curves of the modules at elevated stress temperature, their use to determine the maximum power at 25 degrees C standard test conditions (STC), and distribution statistics for determining degradation rates as a function of stress level. The semi-continuous data obtained by this method clearly show degradation curves of the maximum power, including an incubation phase, rates and extent of degradation, precise time to failure, and partial recovery. Stress tests were performed on crystalline silicon modules at 85% relative humidity and 60 degrees C, 72 degrees C, and 85 degrees C. Activation energy for the mean time to failure (1% relative) of 0.85 eV was determined and a mean time to failure of 8,000 h at 25 degrees C and 85% relative humidity is predicted. No clear trend in maximum degradation as a function of stress temperature was observed.
Overcommunication Strategies of Violating Grice’s Cooperative Principle in Ground Service

Based on four maxims of Grice’s cooperative principle framework, a small scale study is conducted to examine the communication strategies employed by experienced ground service staff. Data have been collected from questionnaires and in –depth interviews with Chinese domestic airlines’ ground staff. This study identifies that the communicative challenges usually happen in service failure and service recovery. And it finds out violation of four maxims of Grice’s cooperative principle is taken as sub-strategies of overinformativeness, telling a white lie, irrelevance, and ambiguity or prolixity. These strategies lead to overcommunication which could be routinely employed for the specific professional purposes of saving the passenger’s face, gaining the passenger’s understanding, reducing conflicts, avoiding complaints, or even establishing a harmonious relationship with passengers. This study moreover exploits that in the ground service communication context, two salient reasons stimulate the overcommunication strategies. Firstly it is to improve the service quality and secondly to enhance the passenger’s satisfaction which are the determining factors to the sustainable development of the airlines. This study is ambitious to seek a new means of understanding and investigating the ground service communication from the perspective of pragmatics. By integrating business communication in ESP context to the classical theory of pragmatics, this study attempts to offer practical suggestions to the frontline communicators.