Miracle-dz/UniFAR-FTUs huggingface dataset:
Far-Ultraviolet Spectra of a Nonradiative Shock Wave in the Cygnus Loop

Spatial and spectral profiles of O VI emission behind a shock wave on the northern edge of the Cygnus Loop were obtained with the Far Ultraviolet Spectroscopic Explorer. The velocity width of the narrowest O VI profile places a tight constraint on the electron-ion and ion-ion thermal equilibration in this 350 km s-1 collisionless shock. Unlike faster shocks in SN 1006 and in the heliosphere, this shock brings oxygen ions and protons to within a factor of 2.5 of the same temperature. Comparison with other shocks suggests that shock speed, rather than Alfvén Mach number, may control the degree of thermal equilibration. We combine the O VI observations with a low-resolution far-UV spectrum from the Hopkins Ultraviolet Telescope, an Hα image, and ROSAT PSPC X-ray data to constrain the preshock density and the structure along the line of sight. As part of this effort, we model the effects of resonance scattering of O VI photons within the shocked gas and compute time-dependent ionization models of the X-ray emissivity. Resonance scattering affects the O VI intensities at the factor of 2 level, and the soft spectrum of the X-ray rim can be mostly attributed to departures from ionization equilibrium. The preshock density is about twice the canonical value for the Cygnus Loop X-ray-emitting shocks.
Two-Dimensional Extended Feature Line Space for Feature Extraction

A novel matrix-based image feature extraction approach, entitled Two-Dimensional Extended Feature Line Space (2DEFLS), is proposed in this paper. Nearest feature line (NFL) is a powerful classifier. Some NFL based subspace algorithms have been put forward recently. In most of the NFL-based subspace learning algorithms, the input samples should be vectors. For image classification tasks, image samples should be transformed to vectors firstly. This process leads to a high computational complexity and also may result in the loss of the geometric feature of samples. The proposed 2DEFLS is a matrix-based algorithm. It aims to minimize the within class scatter of an extended training sample set based on two-dimensional NFL. The experimental results demonstrate the effectiveness of the proposed algorithm.
A Memristive Hyperchaotic System without Equilibrium

A new memristive system is presented in this paper. The peculiarity of the model is that it does not display any equilibria and exhibits periodic, chaotic, and also hyperchaotic dynamics in a particular range of the parameters space. The behavior of the proposed system is investigated through numerical simulations, such as phase portraits, Lyapunov exponents, and Poincaré sections, and circuital implementation confirmed the hyperchaotic dynamic.
Spatially explicit inter-temporal forest management decision under the risk of fire

approved : Claire A. Montgomery Jeffrey L. Arthur This paper presents a framework for analyzing efficient spatial allocation of forest management efforts fuel treatment and harvest under the risk of fire. The framework integrates a fire behavior model and a spatially-explicit stochastic dynamic optimization model. I investigate the effects of spatial interaction across plots during forest fires in particular a spatial externality on efficient allocation of fuel management efforts. This spatial externality is captured in a spatial endogenous risk framework decisions affect a risk distribution over space. By solving computationally for a number of initial bioeconomically heterogeneous landscapes, general insight into implementing spatial allocation of fuel management efforts is derived. Sensitivity analyses are conducted to evaluate various economic and physical environments and their impact on the optimal solutions. Because the optimal spatial allocation of fuel management efforts depends on a spatial distribution of ”value” and ”risk”, the decision generated from a conventional dynamic stand level (or aspatial forest level) model or a simple dynamic spatial model can be suboptimal. Numerical solutions demonstrate that the risk of value loss by fire damage does not necessarily shorten the optimal rotation age because of spatial externalities. These analyses also show that a land manager faces spatially explicit trade-offs in deciding how to investment in fuel treatments.
Evolutionary forwarding games in Delay Tolerant Networks

In this paper, we apply evolutionary games to non-cooperative forwarding control of Delay Tolerant Networks (DTN). We focus our study on the probability to deliver a message from source to destination in a DTN using two-hop routing. We derive the success probability as a function of the competition within a large population of mobiles. In particular, for each message generated by a source, each mobile may take a decision that concerns the strategy by which the mobile participates to the relaying. A mobile that participates receives a unit of reward if it is the first to deliver a copy of the packet to the destination. A utility function is introduced as the difference between a reward unit and the energy cost. We show how the evolution dynamics and the equilibrium behavior (called Evolutionary Stable Strategy — ESS) are influenced by the characteristics of inter contact time, energy expenditure and pricing characteristics. We specialize our analysis also to mechanisms that the source may introduce in order to have the message delivered to the destination with high probability within a given deadline.
Payment Choice, Image Motivation and Contributions to Charity: Evidence from a Field Experiment

This paper examines the impact of payment choice on charitable giving with a door-to-door fund-raising field experiment. Respondents can donate cash only, use debit only, or have both options. Cash donations have lower visibility vis-a-vis solicitors than debit card donations. When debit replaces cash, participation drops by 87 percent. Conditional on participation, donors in the Debit-only treatment give more than donors in Cash-only. In CashD participation decreases compared to Cash-only. Physical attractiveness of both female and male solicitors increases contributions. Solicitor self-confidence has a negative impact. (JEL D14, D64, L31)
Polymorphisms in human telomerase reverse transcriptase (hTERT) gene, gene- gene and gene-smoking interaction with susceptibility to gastric cancer in Chinese Han population

Aims To investigate the association of telomerase reverse transcriptase (TERT) gene polymorphisms and additional gene-gene and gene- environment interaction with gastric cancer (GC) risk. Results GC risk was significantly higher in carriers of G allele of rs2736100 than those with TT genotype (TG+ GG versus TT), adjusted OR (95%CI) =1.68 (1.26-2.17), and higher in carriers of G allele of rs2853669 than those with AA genotype (AG+ GG versus AA), adjusted OR (95%CI) = 1.72 (1.19-2.33). We also found that interaction between rs2736100 and smoking was associated with higher GC risk. Smokers with TG or GG of rs2736100 genotype have elevated GC risk, compared to never- smokers with TT of rs2736100 genotype, OR (95%CI) = 3.12 (1.82 -4.61). Pairwise linkage equilibrium (LD) analysis between SNPs was measured and the D’ value between rs2736100 and rs2736109 was more than 0.8. A haplotype containing the rs2736100- G and rs2736109- A alleles was associated with a statistically increased GC risk (OR= 2.66, 95%CI= 1.28 – 4.12, p<0.0001). Materials and Methods A total of 1088 participants (686 males, 402 females) were selected, including 360 GC patients and 728 normal participants. Logistic regression was performed to investigate association between single nucleotide polymorphisms (SNPs) within TERT gene and GC susceptibility. Generalized multifactor dimensionality reduction (GMDR) model was used to screen gene- gene and gene- environment interaction combinations. Conclusions We found that G allele of rs2736100 and G allele of rs2853669 in TERT gene, interaction between rs2736100 and smoking, and haplotype containing the rs2736100- G and rs2736109- A alleles were all associated with increased GC risk.
2011the International Conference on Advanced Power System Automation and Protection Real-time and Grid-connected Control of Pv Power System

This paper analyzes the equivalent model of photovoltaic cells and then discusses the output characteristics of pho-tovoltaic cells. Using the RTDS (Real Time Digital Simulator) to build a real-time simulation system of grid-connected photo-voltaic power generation that includes three-phase grid-connected PV systems, maximum power point tracking (MPPT) methods for PV arrays, grid-connected control of PV system, inverter control and filter design methods. This model achieves the maximum power tracking by the method of the photovoltaic cells self-optimization perturbation and observation based on the buck circuit. According to the control target of three-phase PWM inverter designing a amplitude and phase control system which makes the network systems realize separately control the active power output and reactive power output, in the meanwhile the DC bus voltage can be limited at the reference value. The simulation results show that the system cannot only track the maximum power point of photovoltaic cells quickly, accurately and effectively, it can realize the stability control of the DC bus voltage. Further, fit out the maximum power curves at different environmental changes (include the Ambient temperature and Illumination intensity) to verify the output characteristics of photovoltaic cells; enhance the grid-connected PV system applications generally.
A GPU-based Method for Massive Simulation of Distributed Behavioral Models with CUDA

This work reports the results of a GPU-based approach for the massive simulation of a distributed behavioral model. In this model an agent has a local perception of the world and then it moves by coordinating with the motion of its neighbors. This carries a very high computational cost in the so-called nearest neighbors search. By leveraging the parallel processing power of the GPU and its new programming model called CUDA, we implemented a spatial hashing where a partitioning of the space is used to accelerate the neighbors search. Through extensive experiments, we demonstrate the effectiveness of our GPU implementation when simulating the motion of high-density agent groups.
Modular re-configurable robot drives

We propose a modular platform for wheeled mobile robots that utilises a 6-edge honey-comb prism as its basic building block to realize robot drives of diverse geometry. In terms of functionality, we designed a specific wheel suspension for a drive-module comb that can utilise both, a standard wheel or a Mecanum wheel. A quick-lock interconnection mechanism for the comb modules allows us to quickly configure/reconfigure various robot drives and enables us to realise autonomous wheeled robots with the ability to connect to other robots or even to reconfigure the robot's geometry. This configuration capability offers many interesting opportunities for robotics research since we can adapt a robot in terms of its kinematic functionality, payload and size.
Measuring Complexity of Aerospace Systems

ion. For this problem, we have chosen to analyze the system in the component level of abstraction. The choice of component level of abstraction is dictated by the fact that both satellites look almost the same at subsystem level of abstraction. Fig. 6 shows the unified system representation for satellite B, which is obtained after mapping the functional graph on the structural graph. Since the analysis is carried out at the component level, hence the weights of the complexity of the components and interactions can be assumed to be one. Figure 4: Satellite A Figure 5: Satellite B 912
Validation of an Internet-based Approach to Cognitive Screening in Multiple Sclerosis

Cognitive impairment affects approximately half of multiple sclerosis (MS) patients. The Multiple Sclerosis Neuropsychological Questionnaire (MSNQ) has previously demonstrated validity for detecting cognitive impairment in MS, and is quick and easy to complete. The objective was to validate an internet version of the MSNQ. The following were completed at home over the internet for 82 MS patients: (a) patient self-report version of the MSNQ (P-MSNQ), (b) informant version of the MSNQ (I-MSNQ), and (c) Centre for Epidemiological Studies Depression Scale (CES-D). Thereafter, patients completed in-office neuropsychological testing using the Brief Repeatable Battery of Neuropsychological Tests (BRB-N). Both the P-MSNQ and I-MSNQ were highly correlated with depression. The best-cut off score on the I-MSNQ was a 26, which gave a sensitivity of 72% and 60% for detecting cognitive impairment on the BRB-N. Given the modest sensitivity and specificity values, the MSNQ is not recommended for neuropsychological screening purposes over the internet. iii Acknowledgments
A New Method for Consequence Finding and Compilation inRestricted

SFK (skip-ltered, kernel) resolution is a new method for nding \interesting" consequences of a rst order clausal theory , namely those in some restricted target language LT. In its more restrictive form, SFK resolution corresponds to a relatively eecient SAT method, directional resolution; in its more general form, to a full prime implicate algorithm, namely Ti-son's. It generalizes both of them by ooering much more exible search, rst order completeness, and a much wider range of inferential capabilities. SFK resolution has many applications: computing \characteristic" clauses for task-speciic languages in abduction, explanation and non-monotonic reasoning (Inoue 1992); obtaining LUB approximations of the input theory (Selman and Kautz 1996) which are of polynomial size; incremental and lazy exact knowledge compilation (del Val 1994); and compilation into a tractable form for restricted target languages, independently of the tractability of inference in the given target language.
The risk of tuberculosis disease among persons with diabetes mellitus: a prospective cohort study.

BACKGROUND Evidence suggests a causal link between diabetes mellitus and tuberculosis risk. However, to date, few studies have used a prospective design to estimate the impact of diabetes on tuberculosis in a general population. In this study, we prospectively investigated the risk of tuberculosis among persons with diabetes stratified by severity. METHODS A cohort study was performed that involved 17,715 Taiwanese persons on whom baseline data were collected during Taiwan's 2001 National Health Interview Survey. Participants' subsequent medical care until December 2004 was captured from the National Health Insurance database. The diagnosis and severity of diabetes were established using self-report, International Classification of Diseases, Ninth Revision, Clinical Modification codes, and pharmacy records; incident tuberculosis disease was identified using these codes and pharmacy records. Covariates were obtained through in-person interviews. We used Cox proportional hazards regression analyses to measure the association between tuberculosis and both diabetes and diabetes severity. RESULTS Diabetes in general and treated diabetes were significantly associated with tuberculosis (adjusted hazard ratio, 2.09 [95% confidence interval {CI}, 1.10-3.95] and 2.60 [95% CI, 1.34-5.03], respectively). Compared with persons without treated diabetes, participants' risk of tuberculosis increased as the number of complications of diabetes mellitus increased (P = .0016), with >3-fold risk among those with ≥ 2 diabetes-related complications (odds ratio, 3.45; 95% CI, 1.59-7.50). Similarly, the risk increased among those with higher Diabetes Complications Severity Index scores (P = .0002). CONCLUSIONS The risk of developing tuberculosis increased among those with increasing diabetes severity.
The Grammar of English Deverbal Compounds and their Meaning

We present an interdisciplinary study on the interaction between the interpretation of noun-noun deverbal compounds (DCs; e.g., task assignment) and the morphosyntactic properties of their deverbal heads in English. Underlying hypotheses from theoretical linguistics are tested with tools and resources from computational linguistics. We start with Grimshaw’s (1990) insight that deverbal nouns are ambiguous between argument-supporting nominal (ASN) readings, which inherit verbal arguments (e.g., the assignment of the tasks), and the less verbal and more lexicalized Result Nominal and Simple Event readings (e.g., a two-page assignment). Following Grimshaw, our hypothesis is that the former will realize object arguments in DCs, while the latter will receive a wider range of interpretations like root compounds headed by non-derived nouns (e.g., chocolate box). Evidence from a large corpus assisted by machine learning techniques confirms this hypothesis, by showing that, besides other features, the realization of internal arguments by deverbal heads outside compounds (i.e., the most distinctive ASN-property in Grimshaw 1990) is a good predictor for an object interpretation of non-heads in DCs.
On the NP Structure and Prenominal Ordering in Korean

The flexibility of Korean NP structures has been well-observed, but there have been few attempts to provide precise syntactic structures. This paper at first reviews the basic distributional properties of Korean prenominal expressions as well as constraints in ordering, and then sketches a constraint-based, lexicalist analysis for Korean NP structures. Arguing for surface-based syntactic structures with more flexible subcategorization requirements, the paper shows that this lexicalist-based analysis can in a simple manner capture the flexible orderings of prenominal expressions as well as generate proper and precise NP structures, without resorting to functional projections.
Comparison of cluster, Grid and Cloud Computing using three different approaches

Nowadays IT world talks too much about the terms of the cluster, grid and the cloud. Increasing attention to these terms not only makes them important but also researchers focus on these technologies in many areas. In fact that they deserve this attention and importance, but majority of the IT people are confusing what they mean and how they differ in using of and their implications for IT perspective. Therefore, this paper discusses the differences and similarities of these terms.
Computational Identification of Novel Genes: Current and Future Perspectives

While it has long been thought that all genomic novelties are derived from the existing material, many genes lacking homology to known genes were found in recent genome projects. Some of these novel genes were proposed to have evolved de novo, ie, out of noncoding sequences, whereas some have been shown to follow a duplication and divergence process. Their discovery called for an extension of the historical hypotheses about gene origination. Besides the theoretical breakthrough, increasing evidence accumulated that novel genes play important roles in evolutionary processes, including adaptation and speciation events. Different techniques are available to identify genes and classify them as novel. Their classification as novel is usually based on their similarity to known genes, or lack thereof, detected by comparative genomics or against databases. Computational approaches are further prime methods that can be based on existing models or leveraging biological evidences from experiments. Identification of novel genes remains however a challenging task. With the constant software and technologies updates, no gold standard, and no available benchmark, evaluation and characterization of genomic novelty is a vibrant field. In this review, the classical and state-of-the-art tools for gene prediction are introduced. The current methods for novel gene detection are presented; the methodological strategies and their limits are discussed along with perspective approaches for further studies.
Early Life Obesity and Diabetes: Origins in Pregnancy

The gestational period represent a critical time for influencing birth weight, infant adiposity and growth rate, all of which are important factors in the development of obesity and diabetes in adolescence and adulthood. Prior to pregnancy, the nutritional status and weight of a mother also has significant influence on these factors. During gestation, numerous maternal factors can have a negative influence on programming of the long-term health of the offspring. Rate of maternal weight gain, glucose intolerance, gestational diabetes and pre-pregnancy BMI all have significant influence on infant size and adiposity at birth. Additionally, these factors are related to the development obesity and its co-morbidities in adolescence and adulthood. Given the rates of obesity and associated health care costs, it is very timely to understand possible fetal origins of obesity and diabetes to help shape interventions.
Airline Schedule Planning: Accomplishments and Opportunities

Plagued by high labor costs, low profitability margins, airspace and airport congestion, high capital and operating costs, security and safety concerns, and complex and large-scale management and operations decisions, the airline industry has armed its planners with sophisticated optimization tools to improve decision making and increase airline profits. In this paper, we describe optimization approaches for airline schedule planning, demonstrating how optimization can facilitate the management of a diverse and finite set of expensive, highly constrained resources. We focus on the art and science of modeling and solving these problems, providing illustrative examples of the associated impacts and challenges, and highlighting effective techniques that might be applicable to problems arising in other industries.
Recovering Signals From Lowpass Data

The problem of recovering a signal from its low frequency components occurs often in practical applications due to the lowpass behavior of many physical systems. Here, we study in detail conditions under which a signal can be determined from its low-frequency content. We focus on signals in shift-invariant spaces generated by multiple generators. For these signals, we derive necessary conditions on the cutoff frequency of the lowpass filter as well as necessary and sufficient conditions on the generators such that signal recovery is possible. When the lowpass content is not sufficient to determine the signal, we propose appropriate pre-processing that can improve the reconstruction ability. In particular, we show that modulating the signal with one or more mixing functions prior to lowpass filtering, can ensure the recovery of the signal in many cases, and reduces the necessary bandwidth of the filter.
Orientations of literacy leadership among elementary school principals: demographic and background trends

Research on administration from the past forty years emphasizes the principal as instructional leader. However, the research community has done little to specifically examine what literacy knowledge principals of elementary buildings need to possess regarding literacy teaching and learning or how districts build literacy leadership capacity. Because federal legislation has focused increased scrutiny on literacy, the role of the elementary school principal as the instructional leader has intensified. In an era of increased accountability, effective literacy leadership is essential to the development and continued improvement of an elementary school. Yet there is a dearth of research regarding what constitutes necessary literacy knowledge for elementary principals, what skills are needed to assume the role of literacy leader, and how districts can provide school literacy knowledge and leadership skill sets to principals. This study focused on the theoretical orientation Iowa elementary principals hold regarding the teaching and learning of reading in the elementary classroom. Survey data were collected and analyzed to determine the instructional preference principals hone for reading instruction, including preferences for phonic, skill, and whole language instruction. Such preferences