The study observes the Pandemic Crisis (Covid 19) that resulted in impacts on the Transportation category in the area National Capital Region. Public transportation is an important aspect of human’s ability to travel to different places whether its personal or business purpose, it’s a part of life that people take for granted and can’t be taken away easily. But due to the pandemic era, people have been careful in their choices, which resulted in the change standard when it comes to public transportation choices. With that said, to understand and observe these impacts, a scenario must be made such as before and after the pandemic designed as an environment for the study to take root. The study has used machine learning called Random Forest Algorithm with the used several parameters to create a prediction model. As for the method in gathering data, a survey of Google Form is utilized to gather 200 participants of the National Capital Region with varying parameters for their choice of public transportation. The machine algorithm has shown satisfactory accuracy of 89.88% and 88.88%. As an important note, it is observed that travel expense has more impact on public transportation choices than other parameters. The Random Forest Algorithm has been utilized in creating classification types of models and can help future researchers improve the machine learning approach.

Since the start of the 2019 pandemic, medical staff and non-medical staff are fighting on the front line in all hospitals worldwide. However, the possibility of healthcare workers’ scarcity due to the increasing medical infection rate is ignored in many recent studies. To prevent such things to happen, the installation of a negative air pressure isolation room is proposed to Norzagaray Municipal Hospital (NMH). Primary parameters such as filtration, pressure management, and dilution ventilation were investigated in SOLIDWORKS simulation software by removing one parameter per simulation. Two existing schemes were simulated, and the primary parameters present were evaluated. Three ventilation design set-ups were designed and the effects of the varying placements of the primary parameters to the airflow pattern in a negative air pressure isolation room were determined. Cost-benefit analysis (CBA) was conducted to determine if the cost of installing the negative air pressure room outweighs its benefit. The set-up where the High efficiency particulate air (HEPA) machine is inside the room is proposed to NMH as this abides by the Department of Health (DOH) memorandum and standards on Airborne Infection Isolation Rooms (AIIRs) and is the most effective of the three set-ups. Results show that filtration filters the infectious particles, pressure management manages the proper airflow direction, and dilution ventilation makes sure there are enough air changes per hour to filter a percentage of infectious particles. In the existing schemes, all the primary parameters were used to contain the infectious particles in the room, however, the effectivity of the filtration also depends on the location of the patient, supply, and exhaust. The most significant effect of the varying placements of the primary parameters can be seen in filtration as only the set-up where the HEPA machine is inside the room was able to filter 100% of the infectious particles. It is also the most profitabl...

Structures built on high seismic areas are likely to experience earthquake liquefaction. This in turn will compromise the integrity of the structures and thus, assessment of the susceptibility to liquefaction is essential. To evaluate the likelihood and severity of earthquake induced liquefaction particularly in the 2nd district of Laguna, 74 geotechnical reports from various locations were collected. Using deterministic approach, safety factors and liquefaction severity index were calculated at different locations to generate liquefaction probability and severity maps. Results showed that there is a wide range of liquefaction severity levels from very low severity of 3.8% of the areas to high severity of 5.06% of the areas. The probability map further showed that an average of 90.49% of the areas are susceptible to liquefaction when an 8.0 earthquake magnitude occurs. The developed maps can be used by site planners and engineers to identify the severity of liquefaction at specific locations and appropriately apply remedial measures in the design of structures.

It is clear that since the COVID-19 pandemic started in the Philippines, education is one of the most affected areas. After more than a year of struggling with different community lockdowns and the alarming consistency with the increasing number of confirmed cases each day, students and teachers are now left with the choice to voice out their frustrations, activism, opinions, and ideas regarding online classes through different social networking sites, most especially Twitter. With the influx of tweets available in the internet sphere, the authors of this study decided to conduct a sentiment analysis to categorize the overall opinions of Filipino citizens about the current state of education after more than a year of adapting with the distance learning practices that are now considered as the new normal. The authors utilized rtweet, a built-in package available in R programming to perform opinion mining on Twitter data collected through the package related to online class during pandemic. Through sentiment lexicons available in R such as bing and afinn, the results show that most of the tweets about online learning in the Philippines turned out to be neutral. The positive responses are 55.77% while 44.23% of the sentiments collected are negative. To evaluate the accuracy rates of results, the authors used three classification techniques namely Naïve Bayes, logistic regression, and random forest. Naïve bayes and logistic regression both show 69.23% accuracy rate and random forest calculated 71.15% accuracy in identifying whether the given tweet is a positive, negative, or neutral sentiment.