This paper aims to study the determinants of the Lean Service System (LSS) on the Operational Performance (OP) of India's mail service in the National Sorting Hub (NSH), Mangaluru, Karnataka, the southern part of India. Measuring the OP in mail service is a big challenge in the postal service industry. Hence, we have conducted a survey, and 150 usable data has measured the impact of Lean Service Practices (LSP), Lean Workplace Environment Practices (LWEP), and Lean Social Practices (LSoP) on the OP. The results are analyzed from the partial least square based structural equation modelling (PLS-SEM) with the support of R programming. The analysis shows that there is positive and significant impact of LSP ( β = 0.380, p < .05), followed by LWEP ( β = 0.281, p < .05), and LSoP ( β = 0.266, p < .05) on OP. The practical effect of the findings of LSS are effectively implemented for enhancing the OP of the business. This research addresses the appropriate empirical model to test LSS in India's postal service industry, which is scant in the existing literature. Moreover, this study helps India Post to review its policy so as to sustain the effectiveness of Lean Service (LS) implementation.
Estimating our sleep quality and state is essential to identify disorders or chronic ailments related to sleep patterns. The authors propose certain methods using machine learning and deep learning algorithms to assess the quality of sleep. The signals and hence the data taken from the wrist actigraphy and data accumulated through sleep research on postmenopausal women are used in this work. This data is preprocessed and used to score the sleep-wake pattern objectively and subjectively. With the availability of multi-ethnic data, both machine learning and deep learning models are created by rigorous training to avoid over-fitting and under-fitting. As the users of wearable active devices are increasing and proven to be a commercially successful model, this work is more relevant to multiple groups across ages.
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