Sleep specialists often conduct manual sleep stage scoring by visually inspecting the patient's neurophysiological signals collected at sleep labs. This is, generally, a very difficult, tedious and time-consuming task. The limitations of manual sleep stage scoring have escalated the demand for developing Automatic Sleep Stage Classification (ASSC) systems. Sleep stage classification refers to identifying the various stages of sleep and is a critical step in an effort to assist physicians in the diagnosis and treatment of related sleep disorders. The aim of this paper is to survey the progress and challenges in various existing Electroencephalogram (EEG) signal-based methods used for sleep stage identification at each phase; including pre-processing, feature extraction and classification; in an attempt to find the research gaps and possibly introduce a reasonable solution. Many of the prior and current related studies use multiple EEG channels, and are based on 30 s or 20 s epoch lengths which affect the feasibility and speed of ASSC for real-time applications. Thus, in this paper, we also present a novel and efficient technique that can be implemented in an embedded hardware device to identify sleep stages using new statistical features applied to 10 s epochs of single-channel EEG signals. In this study, the PhysioNet Sleep European Data Format (EDF) Database was used. The proposed methodology achieves an average classification sensitivity, specificity and accuracy of 89.06%, 98.61% and 93.13%, respectively, when the decision tree classifier is applied. Finally, our new method is compared with those in recently published studies, which reiterates the high classification accuracy performance.
<p>In the current competitive business environment, all companies look for strategies and tools to stabilize in this competition and bring about their growth and enhancement. Organizations have to align themselves as fast as possible with such permanent changes in order to maintain their survival. Brand Personality is one of the helpful tools that organizations employ to retain their current customers, attract new ones, and achieve competitive advantage. Brand personality is also a potential marketing strategy to increase customers’ loyalty towards a brand. Many customers choose products with a brand that is suitable to their personality. This is true about mobile phone customers, as well. The purpose of this study is to explore the relationship between Brand Personality and Customer Loyalty. To this end, variables such as sincerity, excitement, competence, sophistication, and ruggedness were tested as dimensions of brand personality and formed of the research hypotheses. The statistical population included all customers of Samsung Mobile Phone and a random sampling method was used. The required data related to theoretical principles were collected using a historical study such as books and academic journals and the data required to analyze and test the hypotheses were collected through a researcher self-made questionnaire. The obtained results revealed that there is a significant relationship among brand personality dimensions and customer loyalty.</p>
Six Sigma is recognized as an essential tool for continuous improvement of quality. A large number of publications by various authors reflect the interest in this technique. Reviews of literature on Six Sigma have been done in the past by a few authors. However, considering the contributions in the recent times, a more comprehensive review is attempted here. The authors have examined various papers and have proposed a different scheme of classification. In addition, certain gaps that would provide hints for further research in Six Sigma have been identified. As a results the relationship between Six Sigma, Design for Six Sigma (DFSS), and how these two concepts support the quality system for organizational learning and innovation performance have been discussed that would help researchers, academicians and practitioners to take a closer look at the growth, development and applicability of Six Sigma in Design.
This study explored the effect of managerial power on employees' affective commitment using social exchange theory. It was applicable-descriptive and the research sample included 185 managers of the Social Security Organization of Fars Province. The required data was collected via a researcher self-made questionnaire that consisted of two sections of managerial power and affective commitment. Validity of the questionnaire was confirmed by five professors and its reliability was obtained equal to 0.89 via Cronbach's alpha coefficient. The data was analyzed through Amos19 and EQS6.1 software. Two important findings were obtained. First, there was a positive and significant relationship between expert power, legitimate power, referent power, reward power and coercive power with affective commitment. Second, reward power had the highest effect and coercive power had the lowest effect on employees' affective commitment. The findings revealed that desirable perceptions of employees from the manager's power in the framework of social exchange will lead to positive consequences such as affective commitment.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.