A NOVEL APPROACH FOR RECRUITMENT PROCESS IN HUMAN RESOURCES MANAGEMENT

Discovering and using valuable and meaningful data which is hidden in large databases can have strategic importance in the managerial decision making process for organizations to gain competitive advantage. With the increasing data flow, it has become more difficult for organizations to store this data and gain useful knowledge to manage their business operations and functions. Knowledge discovery process that is based on data mining methods has widely been used in business operations and management functions.This paper investigates formal concept analysis which is a powerful tool in knowledge representation and discovery and explains association rule mining based-on formal concept analysis. An experimental study is given for employee selection function ofHRM by using formal concept analysis method to model the qualifications of candidates which are needed for the job position. The qualifications of the candidates are modelled with concept lattices and the qualifications of the candidates are matched with the ones determined in the job specification.

How Complete Are Surveys for Nearby Transiting Hot Jupiters?

Hot Jupiters are a rare and interesting outcome of planet formation. Although more than 500 hot Jupiters (HJs) are known, most of them were discovered by a heterogeneous collection of surveys with selection biases that are difficult to quantify. Currently, our best knowledge of HJ demographics around FGK stars comes from the sample of ≈40 objects detected by the Kepler mission, which have a well-quantified selection function. Using the Kepler results, we simulate the characteristics of the population of nearby transiting HJs. A comparison between the known sample of nearby HJs and simulated magnitude-limited samples leads to four conclusions. (1) The known sample of HJs appears to be ≈75% complete for stars brighter than Gaia G ≤ 10.5, falling to ≲50% for G ≤ 12. (2) There are probably a few undiscovered HJs with host stars brighter than G ≈ 10 located within 10° of the Galactic plane. (3) The period and radius distributions of HJs may differ for F-type hosts (which dominate the nearby sample) and G-type hosts (which dominate the Kepler sample). (4) To obtain a magnitude-limited sample of HJs that is larger than the Kepler sample by an order of magnitude, the limiting magnitude should be approximately G ≈ 12.5. This magnitude limit is within the range for which NASA’s Transiting Exoplanet Survey Satellite can easily detect HJs, presenting the opportunity to greatly expand our knowledge of hot-Jupiter demographics.

Cosmic Flow Measurement and Mock Sampling Algorithm of Cosmicflows-4 Tully−Fisher Catalog

Measurements of cosmic flows enable us to test whether cosmological models can accurately describe the evolution of the density field in the nearby universe. In this paper, we measure the low-order kinematic moments of the cosmic flow field, namely bulk flow and shear moments, using the Cosmicflows-4 Tully−Fisher catalog (CF4TF). To make accurate cosmological inferences with the CF4TF sample, it is important to make realistic mock catalogs. We present the mock sampling algorithm of CF4TF. These mocks can accurately realize the survey geometry and luminosity selection function, enabling researchers to explore how these systematics affect the measurements. These mocks can also be further used to estimate the covariance matrix and errors of the power spectrum and two-point correlation function in future work. In this paper, we use the mocks to test the cosmic flow estimator and find that the measurements are unbiased. The measured bulk flow in the local universe is 376 ± 23 (error) ± 183 (cosmic variance) km s−1 at depth d MLE = 35 Mpc h −1, to the Galactic direction of (l, b) = (298° ± 3°, −6° ± 3°). Both the measured bulk and shear moments are consistent with the concordance Λ Cold Dark Matter cosmological model predictions.

The Kaiser-Rocket effect: three decades and counting

The peculiar motion of the observer, if not accurately accounted for, is bound to induce a well-defined clustering signal in the distribution of galaxies. This signal is related to the Kaiser rocket effect. Here we examine the amplitude and form of this effect, both analytically and numerically, and discuss possible implications for the analysis and interpretation of forthcoming cosmological surveys. For an idealistic cosmic variance dominated full-sky survey with a Gaussian selection function peaked at z ∼ 1.5 it is a > 5σ effect and it can in principle bias very significantly the inference of cosmological parameters, especially for primordial non-Gaussianity. For forthcoming surveys, with realistic masks and selection functions, the Kaiser rocket is not a significant concern for cosmological parameter inference except perhaps for primordial non-Gaussianity studies. However, it is a systematic effect, whose origin, nature and imprint on galaxy maps are well known and thus should be subtracted or mitigated. We present several approaches to do so.

Model Selection in Peptide-receptor Radionuclide Therapy for an Accurate Determination of Time Integrated Activity Coefficients

Calculation of accurate time-integrated activity coefficients (TIACs) is desirable in nuclear medicine dosimetry. The accuracy of the calculated TIACs is highly dependent on the fit function. However, systematic studies of determining a good function for peptide-receptor radionuclide therapy (PRRT) in different patients have not been reported in the literature. The aim of this study was to individually determine the best function for the calculation of TIACs in tumor and kidneys using a model selection based on the goodness of fit criteria and Corrected Akaike Information Criterion (AICc). The data used in this study was pharmacokinetic data of 111In-DOTATATE in tumor and kidneys obtained from 4 PRRT patients. Eleven functions with various parameterizations were formulated to describe the biokinetic data of 111In-DOTATATE in tumor and kidneys. The model selection was performed by choosing the best function from the function with sufficient goodness of fit based on the smallest AICc. Based on the results of model selection, function A 1 -(λ 1+λphys )t was selected as the best function for all tumor and kidneys in patients with meningioma tumors. By using this function, the calculated of TIACs could be more accurate for future patients with meningioma tumor.

improved Master for the LSS: fast and accurate analysis of the two-point power spectra and correlation functions

ABSTRACT We review the methodology for measurements of two-point functions of the cosmological observables, both power spectra and correlation functions. For pseudo-Cℓ estimators, we will argue that the window-weighted overdensity field can yield more optimal measurements as the window acts as an inverse noise weight, an effect that becomes more important for surveys with a variable selection function. We then discuss the impact of approximations made in the Master algorithm and suggest improvements, the iMaster algorithm, which uses the theoretical model to give unbiased results for arbitrarily complex windows provided that the model satisfies weak accuracy conditions. The methodology of iMaster algorithm is also generalized to the correlation functions to reconstruct the binned power spectra, for E/B mode separation, or to properly convolve the correlation functions to account for the scale cuts in the Fourier space model. We also show that the errors in the window estimation lead to both additive and multiplicative effects on the overdensity field. Accurate estimation of window power can be required up to scales of ∼2ℓmax or larger. Mis-estimation of the window power leads to biases in the measured power spectra, which scale as ${\delta C_\ell }\sim M^W_{\ell \ell ^{\prime }}\delta W_{\ell ^{\prime }}$, where the $M^W_{\ell \ell ^{\prime }}$ scales as ∼(2ℓ + 1)Cℓ leading to effects that can be important at high ℓ. While the notation in this paper is geared towards photometric galaxy surveys, the discussion is equally applicable to spectroscopic galaxy, intensity mapping, and Cosmic Microwave Background radiation (CMB) surveys.

A Fuzzy Based Handover Decision Scheme for Mobile Devices Using Predictive Model

Handover usually deals with the mobility of the end users in a mobile network to assure about the ongoing session of a user. It is observed that frequent handover results in call dropping due to latency. In order to overcome this issue, a fuzzy based handover decision scheme for mobile devices using a predictive model is proposed. First, an MFNN (Multi-layer Feed Forward Network) is used to determine the next cell of the user along with best hand off time. To obtain the best access network, multiple-attribute Access Network Selection Function (ANSF) is used. The fuzzy rule is applied by considering the parameter data rate, reliability, signal strength, battery power and mobility as input and the output obtained is the optimal network. The proposed scheme selects the best access network and enhances the quality of services.