The smallest neutrino mass revisited

As is well known, the smallest neutrino mass turns out to be vanishing in the minimal seesaw model, since the effective neutrino mass matrix Mν is of rank two due to the fact that only two heavy right-handed neutrinos are introduced. In this paper, we point out that the one-loop matching condition for the effective dimension-five neutrino mass operator can make an important contribution to the smallest neutrino mass. By using the available one-loop matching condition and two-loop renormalization group equations in the supersymmetric version of the minimal seesaw model, we explicitly calculate the smallest neutrino mass in the case of normal neutrino mass ordering and find m1 ∈ [10−8, 10−10] eV at the Fermi scale ΛF = 91.2 GeV, where the range of m1 results from the uncertainties on the choice of the seesaw scale ΛSS and on the input values of relevant parameters at ΛSS.

Identifying and Prioritizing the Effective Criteria in Selecting Lean Six Sigma Improvement Projects in the Healthcare Sector

The main objective of this study was to identify and prioritize effective criteria in selecting Lean Six Sigma improvement projects in the healthcare and treatment sector in Iran. The present study was an applied research in terms of objective and a descriptive and analytical one according to the research methodology and data collection approach. The research statistical population included experts and managers with experience in the field of implementing the lean six sigma methodology in the field of healthcare and treatment in Iran. We used interviews and questionnaire tools to collect the data. The effective criteria were identified through reviewing previous research, which were then prioritized based on the experts’ opinions using the BWM method. According to the results, out of the six main dimensions and 20 criteria identified, the customer development dimension with a weight of 0.387 and the customer satisfaction criterion with a weight of 0.066 were determined as the most effective dimension and the most effective criterion, respectively. Accordingly, the directors of medical centers and organizations affiliated with the healthcare sector are recommended to pay special attention to these defined criteria of the customer development dimension to effectively implement the lean six sigma methodology and managing an effective customer relationship.

Classification of SSVEP-based BCIs using Genetic Algorithm

Brain computer interface (BCI) systems have been regarded as a new way of communication for humans. In this research, common methods such as wavelet transform are applied in order to extract features. However, genetic algorithm (GA), as an evolutionary method, is used to select features. Finally, classification was done using the two approaches support vector machine (SVM) and Bayesian method. Five features were selected and the accuracy of Bayesian classification was measured to be 80% with dimension reduction. Ultimately, the classification accuracy reached 90.4% using SVM classifier. The results of the study indicate a better feature selection and the effective dimension reduction of these features, as well as a higher percentage of classification accuracy in comparison with other studies.

Energy loss separation in NiFeMo compacts with smoothed powders according to Landgraf’s and Bertotti’s theories

This submitted paper presents the detailed description of the energy loss separation for dc and ac low-frequency magnetic fields of NiFeMo (supermalloy) compacted powder prepared by innovative method of smoothing the surfaces of individual particles. The positive impact of mechanical treatment method on domain wall displacement is explained on the basis of Landgraf approach for dc loss analysis, and the effective dimension for eddy current in ac magnetic field is explained according to Bertotti approach for core loss analysis.

Hyperspectral Band Selection Based on Adaptive Neighborhood Grouping and Local Structure Correlation

Band selection is a direct and effective dimension reduction method and is one of the hotspots in hyperspectral remote sensing research. However, most of the methods ignore the orderliness and correlation of the selected bands and construct band subsets only according to the number of clustering centers desired by band sequencing. To address this issue, this article proposes a band selection method based on adaptive neighborhood grouping and local structure correlation (ANG-LSC). An adaptive subspace method is adopted to segment hyperspectral image cubes in space to avoid obtaining highly correlated subsets. Then, the product of local density and distance factor is utilized to sort each band and select the desired cluster center number. Finally, through the information entropy and correlation analysis of bands in different clusters, the most representative bands are selected from each cluster. Regarding evaluating the effectiveness of the proposed method, comparative experiments with the state-of-the-art methods are conducted on three public hyperspectral datasets. Experimental results demonstrate the superiority and robustness of ANG-LSC.

Development of Standards for Production of an Exit Sign Lighting to Minimize Human Reaction Time - Part I: The Minimum Effective Dimensions

Based on National Fire Safety Codes 303 in Korea, the size of an exit sign lighting to be installed is determined according to space use. Accordingly, a small exit sign lighting can be installed in a large area, which causes problems in the visual recognition of the exit sign lighting by occupants and increases the time required for evacuation. Therefore, in this study, human reaction time measurement experimental tests were conducted in a virtual reality environment to analyze the minimum effective dimension of an exit sign lighting. It was found that the minimum effective dimension of emergency exit sign lighting should be set to a length of 255 mm or more on one side of a 1:1 square display surface.

Classification of SSVEP-based BCIs using Genetic Algorithm

In recent years, brain-computer communication systems have been regarded as a new way of communication for humans. One of the applications of brain-computer communication is the development of systems which facilitates communication. To this end, it is necessary to extract the visually evoked signals from the EEG signal and classify it. In this research, common methods such as wavelet transform are applied in order to extract features. However, genetic algorithm, as an evolutionary method, is used to select features. Finally, after selecting features, the classification was done using the two approaches support vector machine and Bayesian method. Five features were selected and the accuracy of Bayesian classification was measured to be 80% with dimension reduction, and 78% without dimension reduction. Ultimately, the classification accuracy reached 90.4% using SVM classifier. The results of the study indicate a better feature selection and the effective dimension reduction of these features, as well as a higher percentage of classification accuracy in comparison with other studies.