In the Philippines, a country characterized by mountainous landscapes and frequent intense weather events such as typhoons and monsoons, the issue of slope failures has become increasingly pressing. This study investigated the impact of soil cohesion on slope stability using numerical modeling. The SLOPE/W software and Morgenstern-Price method were employed to simulate various slope scenarios with varying cohesion values. Results indicated a strong positive correlation between cohesion and factor of safety (FOS), highlighting the critical role of cohesion in slope stability. Analysis of Variance confirmed the statistical significance of cohesion variations on Factor of Safety. The findings underscore the importance of incorporating cohesion in geotechnical design and slope management, especially in regions like the Philippines with diverse topography. Future research should explore the combined effects of other soil properties and validate numerical results through field testing. By considering cohesion, engineers can optimize slope safety and minimize the risks of slope failures.

Eggshells, which are mainly composed of calcium carbonate, have been considered as alternative filler in polymer composites to reduce cost and improve some of the material properties. One of the biofillers that has not yet widely studied for its potential use in rubber is quail eggshell. For this research study, the effects of quail eggshell filler on the abrasion resistance and thermal degradation of room temperature vulcanizing (RTV) silicone rubber were investigated. Samples with 0, 5, 10 and 15 wt. % quail eggshell filler were prepared through manual mixing and open molding process. Abrasion resistance test was performed based on ASTM D4060 while thermogravimetric-differential thermal analysis was utilized to examine thermal degradation. Results revealed that the abrasion resistance of silicone rubber increases as the eggshell filler content rises from 0 to 15 wt. %. The silicone rubber with 15 wt. % filler achieved the best abrasion resistance with a mass loss of about 44.7 % less than the sample without filler. For the thermal analysis, the sample with 15 wt. % eggshell filler achieved a higher peak degradation temperature of about 605.61 oC as compared to the plain sample (511.25 oC) but the latter appears to resist thermal degradation better at high temperatures.

Engineering students face challenges of on-time successful degree completion. Universities incorporate academic advising as a solution to these challenges. Decision support systems enhance the effectivity of academic advising. Combined with machine learning, it can predict future student performance providing useful information. Compared to common ‘black box’ models, linguistic rules provide better interpretation and insight discovery. However, existing models often use positive predictors of academic excellence, with limited consideration on factors of negative effect. This work, therefore, generates linguistic rules for academic advising based on three predictors using rough set theory (RST) and then compared with artificial neural network (ANN) for benchmarking. Forty-eight samples of mechanical engineering students taking up machine design were considered. RST attained accuracy of 72.92% while ANN attained 66.66%. The model generated 13 linguistic rules, having reflected unrealized insights. The findings from this study may be utilized by academic advisers for pattern recognition, in identifying ‘at-risk’ students.

Decision-making in choosing a degree in engineering, especially for women, involves trade-offs between numerous conflicting objectives. To increase the number of female students enrolling in the Mechanical Engineering Department of Far Eastern University, a survey was conducted among the 30 female engineering students of the Mechanical Engineering Department of Far Eastern University, Institute of Technology. In this study, the objective is to determine the factor that affects female students in choosing engineering field. A multi-criteria decision analytical process was done using a pairwise comparison matrix wherein the goal is to rank the n alternatives. The results of the study revealed that 39.48% of their decision is based on their own choice. It is followed by the influence of the economy with 19%, while the influence of family, available opportunity and the impact of the latest trend have close relative weights, with value within 10% to 12%. Lastly, peer pressure had the least influence with relative weights of 7%. Since personal choice is based on individual's skills, it is recommended that universities should invest in various activities and workshops during their promotional campaign. This will help incoming college students to familiarized and be confident to include engineering in their degree of choice.

In this paper, the analysis of metal and plastic straw choice of individuals residing in Metro Manila is conducted using discrete choice modelling. The mathematical model was correlated with the key factors determined in the study. Using the RStudio software as tool, a discrete choice model is generated. The key factors for straw choice of plastic and metal were hygiene, comfortability, trend, habituation, value, and willingness to buy their choice of straw.