Miracle-dz/UniFAR-FTUs huggingface dataset:
ESTIMATING Ms FROM SHORT-PERIOD ( < 10 SEC ) RAYLEIGH WAVES

Surface wave magnitude (Ms) estimation for small events recorded at near-regional distances will often require a magnitude scale designed for Rayleigh waves with periods less than 10 seconds. During the past year, we have examined the performance of applying two previously published Ms scales on 7-second Rayleigh waves recorded at regional distances. First, we modified the Marshall and Basham (1972) Ms scale, originally defined for periods greater than 10 seconds, which was developed to estimate surface wave magnitudes for short-period Rayleigh waves from earthquakes and explosions on or near the Nevada Test Site (NTS). We refer to this modification as Ms [M+B;7], and we have used short-period, high-quality dispersion curves to determine empirical path corrections for the 7-second Rayleigh waves at the stations MNV, LAC, ELK, and KNB. We have also examined the performance of the Rezapour and Pearce (1998) formula, developed using theoretical distance corrections and surface wave observations with periods greater than 10 seconds, for 7-second Rayleigh waves, Ms [R+P;7], recorded from the same dataset. The results demonstrate that both formulas can be used to estimate Ms for nuclear explosions and earthquakes over a wider magnitude distribution than is possible using conventional techniques developed for 20 second Rayleigh waves. These Ms(7) values scale consistently with other Ms studies at regional and teleseismic distances with the variance described by a constant offset; however, the offset for the Ms [M+B;7] estimates is over one magnitude unit closer to the teleseismic values than the Ms [R+P;7] values. Using our technique, it is possible to employ a near-regional single-station or sparse network to estimate surface wave magnitudes, thus allowing quantification of the size of both small earthquakes and explosions. Finally, we used a jackknife technique to determine the false alarm rates for the Ms [M+B;7]-mb discriminant for this region, and found that the probability of misclassifying an earthquake as an explosion is 10% while the probability of classifying an explosion as an earthquake was determined to be 1.2%. The misclassification probabilities are slightly higher for the Ms [R+P;7] estimates. We recently initiated a study aimed at examining the transportability of short-period Ms to the Lop Nor test site. We developed averaged dispersion curves from large (mb>5.5) nuclear tests at Lop Nor to generate path corrections for the stations AAK, BRVK, KUR, MAK, and TLY. We then estimated short-period Ms for nuclear explosions and earthquakes at Lop Nor and determined that transportability of the Ms[M+B;7] -mb discriminant is more complicated due to deeper earthquakes at Lop Nor than at NTS. However, by using the period of maximum amplitude, instead of restricting ourselves to 7-seconds, and by calibrating path corrections to periods less than 10 seconds, we were able to improve the separation between the earthquake and explosion populations at Lop Nor using non-conventional estimates. 25th Seismic Research Review Nuclear Explosion Monitoring: Building the Knowledge Base
The Use of Data Mining for Strategic Management: A Case Study on Mining Association Rules in Student Information System / Upotreba rudarenja podataka u strateškom menadžmentu: analiza slučaja upotrebe pravila pridruživanja rudarenja podataka u informacijs

Abstract In today’s competitive conditions changes in business environment and business structures make strategic management an effective form of management for business and organizations. Strategic management is a current management strategy that requires setting of the appropriate strategies, plans and applications and putting them into action in order to reach the aims and goals of organizations. The process of strategic management involves setting the company’s vision, mission and objectives, determining the competitive position, and the evaluation of results obtained by strategy selection, development and application. In the application of activities related to the strategic management of business processes, the discipline of data mining, which can be defined as the process of extracting useful and meaningful patterns from large volumes of data, emerges as a viable method. In this study, strategic management and data mining disciplines and their basic concepts and applications are introduced. Apart from that, data mining methods in the context of strategic management are taken into consideration. In addition, a sample case study about the use of association rule mining algorithms in student information systems data will be presented. Key words: business intelligence; data mining; educational data; knowledge discovery. --- Sažetak U danasnje vrijeme kada je konkurencija svugdje jaka, promjene u poslovnom okruženju i poslovnim strukturama cine strateski menadžment ucinkovitim oblikom menadžmenta u kompanijama i organizacijama. Strateski menadžment je suvremena strategija koja zahtijeva postavljanje odgovarajucih strategija, planova i programa, kao i njihovo provođenje da bi organizacije postigle svoje ciljeve. Proces strateskog menadžmenta podrazumijeva određivanje vizije, misije i ciljeva kompanije, određivanje njezine konkurentnosti i evaluaciju rezultata dobivenu odabirom, razvojem i primjenom strategije. Primjenom aktivnosti povezanih sa strateskim menadžmentom u poslovnim procesima disciplina rudarenja podataka pokazala se jako uspjesnom metodom. Ona se može definirati kao proces izdvajanja korisnih i smislenih uzoraka iz velicinom golemih podataka. U ovom istraživanju prikazuju se discipline strateskog menadžmenta i rudarenja podataka, kao i njihovi osnovni pojmovi i primjena. Osim toga, u obzir su uzete metode rudarenja podataka u kontekstu strateskog menadžmenta. Uz to ce se jos prezentirati i primjer analize slucaja o upotrebi algoritama pravila pridruživanja u rudarenju podataka u sustavu podataka o studentima. Kljucne rijeci: otkrivanje znanja; poslovna inteligencija; podaci o obrazovanju; rudarenje podataka.
Comparison of Estimators for the National Resources Inventory Calibration Study

The National Resources Inventory (NRI) is a longitudinal survey of non-federal lands in the U.S. A new protocol for measuring the extent of developed land was implemented in 2003. In 2005, an experiment was conducted to estimate the relation between measurements obtained from the new and former procedures. We propose a model for the data from the calibration experiment and evaluate estimators of model parameters through simulation. The estimation procedure requires an estimator of the unknown covariance matrix of a vector of moments. One way to estimate this covariance matrix is to use the moments of the normal distribution. The delete-one jackknife provides a non-parametric alternative. The results of the simulation study indicate that both estimators of model parameters are approximately unbiased and that the estimator based on the jackknife covariance matrix is more variable than the estimator based on the normal moment covariance matrix. We construct two estimators of the variance of the estimator based on the normal moment covariance matrix. The first continues to use the moments of the normal distribution to estimate the unknown covariance matrix. The second variance estimator uses the jackknife estimator in conjunction with the normal moment estimator. Both estimators are approximately unbiased for variances of estimators of the slope and intercept in the linear calibration equation. Skewness leads to a downward bias in the normal-based variance estimator for variance components.
in a monogamous passerine bird Pairing context determines condition-dependence of song rate

References http://rspb.royalsocietypublishing. org/content/280/1753/20122177.full. html#ref-list-1 This article cites 64 articles, 14 of which can be accessed free Subject collections (1359 articles) evolution  (892 articles) behaviour  Articles on similar topics can be found in the following collections Email alerting service here right-hand corner of the article or click Receive free email alerts when new articles cite this article sign up in the box at the top
Composition and Antioxidant Properties of Spanish EVOO Regarding Cultivar, Harvest Year and Crop Stage

The health benefits of extra virgin olive oil (EVOO) are related with its chemical composition and the presence of bioactive compounds with antioxidant properties. The aim of this study was evaluate antioxidant compounds (pigments, CoQ10 and phenolic compounds) and antioxidant properties of EVOO from the same region comparing different cultivars (Hojiblanca and Arbequina), harvest year and crop stage. Antioxidant properties of oils were studied before and after a gastrointestinal digestion process, by in vitro assays (DPPH, ABTS and FRAP) and antioxidant markers in Caco-2 cells (reactive oxygen species production). The content of bioactive compounds measured was significantly affected by cultivar and harvest year (except for carotenoids) and by the crop stage (except for coenzyme Q10). Higher amount of coenzyme Q10 was observed in Hojiblanca than in Arbequina EVOO. Total phenol content and antioxidant properties were also different depending on cultivar and harvest year and the in vitro digestion process strongly improved antioxidant marker values. Antioxidant potential in bioaccessible fractions was mainly related with content of coenzyme Q10 and phenolic compounds in EVOO. Chemometric analysis showed that the oils were clearly classified by cultivars, harvest and crop stage, according with chemical composition and antioxidant activity analyzed in the present study
Range Estimator , Small Area Estimation , Design-based , Empirical Bayes , Bayes

Interest in state estimates has increased over the past year with the passage of block grants ~ to the states replacing certain welfare programs. Currently two federal demographic surveys are producing estimates for every state and more may follow. The Current Population Survey (CPS) has been producing state estimates since the late 1970s, originally in response to the CETA act which required accurate statespecific labor force data to distribute federal funds. The National Immunization Survey has been producing state estimates since 1984, in response to President Clinton's immunization initiative. Other state specific surveys are under consideration or partial implementation (such as the BRFSS). As these surveys are analyzed, it is frequently overlooked that the properties of an ensemble of statistics can be as important as the properties of each specific state estimator. Important exceptions to this general neglect include Louis (1984), Spjotvoll and Thomsen (1987), Lahiri (1990), and Ghosh (1992). Ghosh and Rao (1994) nicely summarized this earlier work in Section 7.2 of their paper. It is important to consider the properties of the ensemble since states at one extreme are punished (through unfavorable publicity or lack of additional funds) while states at the other extreme are rewarded. Some statistics that depend on the ensemble include the rank ordering, the minimum, the maximum, the range, and percentiles. In this paper, we focus on the range. If there is a large range, then there will be calls for action to address the range and debates on the reasons for the disparity among the states. Much the same thing can happen in an industrial plant where some output from different sites is tested for quality in some way. Managers of sites with low quality ratings get punished and those with high ratings get rewarded. estimates should be published that are expected to have the correct range across the states or other domains of analytic interest. Concern over the range was first voiced to one of the authors in the early 1980s by Barbara Bailar, then an Associate Director at the U. S. Bureau of the Census. When sample sizes are large for each state, the range of the sample means is approximately correct for the range of the true parameters. However, when the sample sizes per state are small and the natural variability among the states is small, the range in the sample means can be seriously positively biased. Beverly Causey developed an unpublished proof that this was true, but a solution to the problem alluded statisticians at the Bureau.
Sisaye and Birnberg Accounting Innovations and Organizational Learning 668623-1 ACCOUNTING INNOVATIONS AND ORGANIZATIONAL LEARNING: LESSONS FROM THE SOCIOLOGY OF ADOPTION-DIFFUSION RESEARCH

The adoption-diffusion research in sociology describes the process whereby organizational innovation is planned and implemented. Organizational learning describes the type of learning: incremental single-loop or transformational doubleloop that is required to successfully adopt and diffuse the innovation. The success of management accounting innovations depends on the types of learning strategy that the organization adopts to implement the innovations. We suggest that the innovation process in management accounting systems can be either autonomousdivision or transformational-organizational wide focused intervention strategies for change. Accordingly, the scope of the change, autonomous or systemic, affects the mode of organizational learning associated with either single and/or double loop learning strategies.
1986: CATEGORICAL DATA ANALYSIS FOR COMPLEX SURVEYS

A class of generalized score tests for the analysis of multidimensional contingency tables from complex survey data is presented. The tests are asymptotically optimal and robust. Furthermore, these tests do not have the problems of instability that are sometimes encountered by the use of Wald type statistics in analysing these data. These problems are eliminated by working with the principal components of the observed count vector. The theory is used to analyze data from the October 1980 Canadian Labour Force Survey.
A Wideband Balanced AlGaN Low Noise Amplifier for Tra

A 3-16 GHz wideband AlGaN/GaN high electron mobility transistor (HEMT) low noise amplifier (LNA), using balanced configuration with a coplanar waveguide (CPW) Lange coupler, is designed and fabricated. The LNA shows a minimum noise figure of 4 dB with associated gain of 20 dB and gain flatness of +/ 3 dB across the 3 16 GHz frequency range. This balanced GaN LNA is suitable for transceiver front-ends due to the low input/output VSWR, high stability and redundancy, as well as, high power handling capability of GaN HEMTs. The design, fabrication and characterization results of the GaN HEMT balanced amplifier are described together with the details of design and characteristics of the individual LNAs and the CPW coupler.
MAS Model of the Level Crossing Ales

The paper introduces a new unpublished version of the multi-agent model of the level crossing system and summarizes research work that has been done since release of the previous concept model in late 2005. The multi-agent approach allows effective modelling of the complex control process of traffic flows from two different kinds of transport. The model respects basic function safety requirements and its graphic design truly corresponds to the Slovak/Czech-like road-rail interface. Code of the model was designed using the hybrid compiler/interpreter NetLogo 3.1. 4. Through different initial settings traffic flows for railway and/or road traffic may be defined and different parameters calculated and observed based on simulation. Partial attention is paid to brief survey of newly implemented functions. Within conclusions the author also presents some further improvements and intentions of the future research.
Site-Dependent Response of Singapore Buildings to Long-Distance Sumatra Earthquakes

Singapore is believed to be located in an aseismic region. However, tremors caused by distant Sumatra earthquakes have reportedly been felt in Singapore for many years. Based on previous studies for Singapore, the maximum credible earthquakes (MCEs) from Sumatra have been hypothesized to be a subduction earthquake (Mw = 9.0) and a strike-slip earthquake (Mw = 7.5). Response at a soft soil site in Singapore to the synthetic bedrock motions corresponding to these MCEs are simulated using a one-dimensional wave propagation method based on the equivalent-linear technique. A typical high-rise residential building in Singapore is analyzed to study its responses subjected to the MCE ground motions at both the rock site and the soft soil site. The results show that the base shear force ratios would exceed the local code requirement in terms of the notional horizontal load for buildings.
Using the t-distribution in Small Area Estimation : An Application to SAIPE State Poverty Models

The Census Bureau’s Small Area Income and Poverty Estimates (SAIPE) program produces state age-group (0-4, 5-17, 18-64, 65+) poverty ratio estimates from Bayesian treatment (Bell 1999) of a Fay-Herriot model (Fay and Herriot 1979) applied to direct state poverty ratio estimates from the Current Population Survey (CPS) Annual Social and Economic Supplement (ASEC, formerly known as the CPS March income supplement). The models borrow information from regression variables related to poverty that are constructed from administrative records data, as well as age group poverty ratio estimates from the previous decennial census. Estimates are identified by the “income year” (IY), which refers to the year for which income is reported in the ASEC. Since 2001, the CPS ASEC sample size has been about 100,000 households. Further information is available on the SAIPE web site at www.census. gov/hhes/www/saipe/index.html. For simplicity, in what follows we shorten references to “CPS ASEC” to just “CPS. ” In recent years supplementary surveys for the American Community Survey (ACS) have also provided state poverty estimates. The ACS asks essentially the same questions as previous decennial census long form surveys, and is replacing the long form, but with the data collection spread continuously throughout the decade. The supplementary surveys have had sample sizes of about 800,000 addresses, significantly larger than the CPS. Further information on the ACS may be found at www.census. gov/acs/www/. The ACS procedures for collecting income data differ from those of the CPS. ACS collects income data continuously with a reference period of the pre-
Improving the Resource Utilisation in MapReduce

MapReduce has gained in widespread popularity over recent years. It achieves great scalability by subdividing jobs into tasks, distributing the tasks across the cluster and executing them in parallel. Since the advent of MapReduce, various effort has been made to improve the cluster resource utilisation. Current scheduling methods mostly focus on the resource sharing policy amongst jobs. However, resources in MapReduce are allocated to tasks rather than directly to jobs, yet work from the task point of view is relatively lacking. In this thesis, I review a few recent advances that delve into the resource allocations on the task level. Despite their advantages, none of those approaches answer the questions raised by various workload patterns, such as CPU-intensive and I/Ointensive, in homogeneous environments. As such, I propose a Finer Grained CPU Scheduler that effectively improves the CPU utilisation, yet does not over-stress the CPU resources, by taking into account the diverse CPU requirements of tasks. Experiments conducted on a Hadoop cluster demonstrate that compared to stateof-art approach YARN, the Finer Grained Scheduler significantly improves the throughput of CPU-intensive workloads without compromising the performance of I/O-intensive ones. Keyword: MapReduce, CPU, resource utilisation, scheduling
Stencil-based applications tuning for multi-core architectures

1 Informatics Institute (INF), Federal University of Rio Grande do Sul (UFRGS), Av. Bento Gonçalves, 9500, Campus do Vale, 91501-970, Porto Alegre, Brazil {victor.martinez,navaux}@inf.ufrgs. br 2 BRGM, 3 Av. Claude Guillemin, Orléans, France {f.dupros,h.aochi}@brgm.fr 3 Department of Informatics and Statistics (INE), Federal University of Santa Catarina (UFSC), Campus Reitor João David Ferreira Lima, Trindade, 88040-970, Florianópolis, Brazil marcio.castro@ufsc.br
A simulation-based study on the application of artificial neural networks to the NIR spectroscopic measurement of blood glucose

Diabetes Mellitus is a major health problem which affects about 200 million people worldwide. Diabetics require their blood glucose levels to be kept within the normal range in order to prevent diabetes-related complications from occurring. Blood glucose measurement is therefore of vital importance. The current glucose measurement techniques are, however, painful, inconvenient and episodic. This document provides an investigation into the use of near-infrared spectroscopy for continuous, non-invasive measurement of blood glucose. Artificial neural networks are used for the development of multivariate calibration models which predict glucose concentrations based on the near-infrared spectral data. Simulations have been performed which make use of simulated spectral data generated from the characteristic spectra of many of the major components of human blood. The simulations show that artificial neural networks are capable of predicting the glucose concentrations of complex aqueous solutions with clinically relevant accuracy. The effect of interference, such as temperature changes, pathlength variations, measurement noise and absorption due other analytes, has been investigated and modelled. The artificial neural network calibration models are capable of providing acceptably accurate predictions in the presence of multiple forms of interference. It was found that the performance of the measurement technique can be improved through careful selection of the optical pathlength and wavelength range for the spectroscopic measurements, and by using preprocessing techniques to reduce the effect of interference. Although the simulations suggest that near-infrared spectroscopy is a promising method of blood glucose measurement, which could greatly improve the quality of life of diabetics, many further issues must be resolved before the long-term goal of developing a continuous non-invasive home glucose monitor can be achieved.
IDENTIFICATION OF THE MODAL PROPERTIES OF AN HISTORIC MASONRY CLOCK TOWER

The aim of the paper is to describe the non-destructive tests performed on the clock tower of the Castle of Trani (Bari, Italy). The tower, built in 1848, was realized in tufa masonry (Stone of Trani); it is about 9 meters high and has a square plan with a side of about 3.9 meters; it is built on the principal enter of the Castle and supported by a barrel vault reinforced with an arch. The non-destructive monitoring was performed by using specific accelerometers placed on the structure at different levels for measuring the acceleration in different points. The particular squat structure of the clock tower suggested the authors to make not only the traditional monitoring, with only environmental actions, but also forced tests by mean of a vibrodine adhoc designed and realized, for determining the building modal parameters. All the phases and the procedures of the experimental monitoring are described and the dynamic identification of the building modal parameters (the frequencies and their corresponding mode shapes) is presented discussing the results in both the operative conditions and the effects of the vibrodine excitation.
On Small Area Prediction Interval Problems

Empirical best linear unbiased prediction (EBLUP) method uses a linear mixed model in combining information from different sources of information. This method is particularly useful in small area problems. The variability of an EBLUP is measured by the mean squared prediction error (MSPE), and interval estimates are generally constructed using estimates of the MSPE. Such methods have shortcomings like undercoverage, excessive length and lack of interpretability. We propose a resampling driven approach, and obtain coverage accuracy of O(d3n−3/2), where d is the number of parameters and n the number of observations. Simulation results demonstrate the superiority of our method over the existing ones.