<p>This paper, introduced an expression of Economic Efficiency Measure (EEM) to permit quick evaluation for replacement of faulty induction motor with alternative (new or refurbished motor) for lowest life-cycle cost based on efficiency and rated-load conditions. This approach, simplifies the process for evaluating the energy efficiency to mere proportionate factor called as EEM. During the operating phase, the motor losses correspond to extra energy consumption, based on various parameters like motor operating conditions, operating hours, operating costs, fault factor, depreciation factor and fixed costs.  The approach is effective in addressing the global issue on replacement of the faulty motor that needs a comprehensive analysis and mathematical expression. Compared to other alternatives the EEM provides a simple but effective and reliable means to asses, the feasibility of replacing or refurbishing the faulty motor. A detail analysis here would establish how much the present approach is effective in determining the replacement for a faulty induction motor either by a new one or refurbished one of corresponding rating.<strong></strong></p>

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An essential component of a patient's follow-up is a medical record. It includes opinions, prescriptions, analyses, and all patient data of healthcare professionals. Process of sharing as well as managing this file involves a number of players, including patient, doctor, and pharmacist. Electronic medical record (EMR) can be accessed by any authorized individual from any location, and data are shared among various health service providers. Using blockchain machine learning techniques, this study proposes a novel method for personal health records (PHR) -based data analysis and network security enhancement. Variational Boltzmann spatio encoder neural networks were utilized for the data analysis of personal health records. The decentralized blockchain architecture enhances network security. Based on network security and data analysis, the experimental analysis is conducted in terms of random accuracy 81%, specificity55%, latency 62%, QoS 52%, and computational cost 41%.