An attachable exoskeleton in backpacks for postural correction by using pressure sensors has been designed and implemented. The system determines the changes in body posture and the selsyn control for the proper posture of shoulder and spine correction. The design of the prototype is based on an exoskeleton which is a backpack, having straps with a selsyn motor, pressure sensors, and portable battery or power bank. The system's functionality and reliability were tested by recording the slouching angle of five test subjects. The study also aims to monitor the improvement of posture in the duration of 2 weeks upon the usage of the system. Pre-test and post-test of posture were evaluated by the physical therapist for improvement in posture and showed a significant reduction of the number of slouches per day for the five test subjects. The four test subjects failed to reach more than 40% reduction of number of slouches, but one subject attained 39.4% reduction. The system is an active device in reminding the subjects to maintain proper posture while most of the posture correctors available in the market are passive. The prototype was able to accomplish what it was designed and created for, which is to reduce the number of slouches for the improvement of the user's posture.

Determination of relay pairs from breakpoints in a given network is essential for maintaining the current protection system. Pairs of relays as primary or backup maintain the operation of the protection scheme within its zone of protection in tandem. All calculations and assumptions that are made in protection systems are based on breakpoints. It is inadequately documented that machine learning can be used to determine breakpoint sets and relay pairs. This paper presents the implementation of supervised decision tree machine learning approach for determining directional overcurrent relay breakpoint set in 3-bus networks. Using the one-hot encoding method, 45 input features are extracted from a matrix derived from 3-bus, 5-line network data. Bayesian optimization is used to further optimize the hyperparameters of each model for each of the break point set outputs. Tree diagrams are also provided here to assist in the interpretation of the decision rule resulting from the regression analysis. Experiment tests indicated that the proposed method shows promising results in determining breakpoint set in terms of RMSE.

The scope of this study folds out the terms of electrical power output generated by the combustion of methane gas in the production of the portable anaerobic digester in kilo Watthour. When the gas flow meter indicated that there is sufficient gas to enter the generator, gas chromatography will again be conducted this time to ensure that the entering gas is really methane. Since there is small load to be powered and only 0.3 m3 of methane that would enter the generator, 1 kW generator would be used to yield sufficient energy which is equivalent to theoretical value of 4.5811 kWh (usable energy) as further explained.