The accumulation of waste tires leads to environmental degradation caused by uncontrolled dumping in landfills, which are prone to fire and emit harmful gases like carcinogens. Reusing this as reinforcement to self-compacting concrete (SCC) is an alternative way to address the issue. For over a decade, SCC emerged in the construction industry due to its enhanced mechanical properties and capacity to self-consolidate on its own. However, there is still limited literature describing the behavior of SCC with tire waste steel fiber (TWSF). This study provides an overview of the extraction, quantification, geometric characterization, surface characterization, and application of TWSF to self-compacting concrete to determine workability and the compressive strength of SCC with TWSF. A total of five mixes were prepared, including the control noted as SCC without fiber and SCC with TWSF, with fiber content ranging from 0.7 %, 1 %, 2 %, and 3 %. The fresh properties were evaluated using the European Federation for Specialist Construction Chemicals and Concrete (EFNARC) standards such as slump flow test, T500, L-Box, and wet sieving or GTM Screen Stability Test. In addition, the compressive strength was determined after 28 days. The investigation reveals that these fibers can be retrieved in three ways: manually cutting the tire's edge, using a specialized machine to pluck the fibers, or incinerating them. It was projected that 4.85 - 7.16 x 105 t of TWSF might be generated annually. The result of the inclusion of TWSF in SCC does not significantly affect the workability. However, there is a reduction in the passing ability of about 11.713 % and 186.75 % for GTM screen stability, but all mixes are still within the acceptable ranges specified on the EFNARC standard. In contrast, the results reveal that adding 3 % TWSF to SCC enhances compressive by 31 %, which might be due to the fiber's uneven surface, increasing the bond between the fiber and concrete. As a result, the TWSF can be utilized to strengthen the SCC and fully applied in the construction industry. Additionally, it is advantageous to combine TWSF with SCC to extend its life resulting in lower carbon emissions produced during the production processes.

Higher educational institutions have a responsibility and commitment to deliver employable graduates as it impacts their well-being and the economy. This study compared the accuracy of several classification algorithms to build an ensemble prediction model capable of forecasting graduates' employability using extensive data mining techniques. Based on the evaluation metrics, an ensemble model composed of Random Forest (RF), Support Vector Machines (SVM), and Naïve Bayes (NB) achieved the highest cross-validated accuracy score of 93.33%. Association rule mining and permutation feature importance analysis from 500 graduates of the electronics engineering program of a university revealed that grit is firmly attributed to employability, including the capabilities to acquire technical skills and professional certifications. Thus, the knowledge gained can be used to develop a range of policies, initiatives, and strategies to increase students' employment prospects.

The major problem when it comes to homogeneous earth dams is the slope stability of the dam. Geo Studio is a software using finite element analysis to evaluate the performance of hydraulic structures. The simulation software can satisfactorily analyze of different conditions, soil parameters, analysis methods and applications. This study aims to simulate different combination designs of a homogeneous earth dam. It will obtain the factor of safety on each combination design of a homogeneous earth dam for slope stability due to downstream flow. A total of 98 simulations were analyzed to check the factor of safety (FOS) of the different soil properties and slope ratio combinations. Throughout all the 98 simulations there is a change in the values of the factor of safety depending on the slope and the soil type used. In the simulation data the highest value of the factor of safety of 2.890 is from the combination of slope of 2V:1H and MH (silt with a high plasticity), while the lowest value of factor of safety of 0.454 is from the combination of slope 2:1 and SP (poorly graded, small silt). This study uses Pearson’s R Correlation method to solve for the relationship between the slope and the factor of safety. The computed r value is 0.616 and is greater than the r tabular value of 0.1946 with 96 degrees of freedom and 0.05 level of significance. It is recommended using the slope 1V:2H since it has the widest base, and has a desirable value for factor of safety, which would prevent the susceptibility to failure of the dam.

The liver is the human body's largest internal organ. Globally, liver disease is considered the cause of approximately 2 million yearly death – whereas the 11th and 16th worldwide leading causes of death are cirrhosis and liver cancer. In the Philippines, according to the Department of Health (DOH), liver cancer is ranked as the 3rd leading cause of death. In most cases, surgery may be considered a possible cure if detected at an early stage. However, there is no efficient early detection method for liver cancer. In this paper, multiple machine learning methodologies are modeled to provide diagnosis classification of liver disease based on the laboratory parameter readings. Based on the results for all models, the most accurate prediction is made by ANN at 89%, followed by SVM at 79.5%. The results establish that AI-based machine learning approaches may be utilized for assisting medical-related diagnosis.

The Philippines has both private and health care facilities, and as far as better health is central to human happiness and well-being [12]. It also makes an important contribution to economic progress, as healthy populations live longer and happier, are more productive, and save more lives [5]. Amongst factors that influence health status and a country's ability to provide quality health services for its people [15]. The Manila City is one of the established cities in Metro Manila named as the capital of the Philippines. It is situated as the center of everything, with a total population of almost 13.9 million people, a good place for commercial and capital investments since Manila City is near to everything. With the advent of innovation, Philippines climbs in as readiness for technological change; they say that Southeast Asia, the Philippines is the fourth most tech-ready economy [3]. A sound digital ecosystem concept should be applied to preserve a decent approach to improving the health and wellness of each constituent in the City of Manila by ensuring that all Filipinos have access to suitable health care through functional service delivery networks. A crucial first step in ensuring that all Manilenos comprehend the advantages of good health and may refer to the city as "Healthy Manila City" is to involve both the public and private sectors in the city.

Corrosion is one of the biggest problems of reinforced concrete structures prone to high chloride environments such as ports and harbors. Due to a lack of studies that can support the use of steel fiber reinforced self-compacting concrete, researchers are still in dispute regarding the effect of using steel fibers in chloride-rich environments. This paper explores the use of neural network modeling to precisely predict and further analyze this problem. Twenty-six different mixtures of steel fiber reinforced self-compacting concrete with varying amounts of cement, water-cement ratio, superplasticizer, and steel fiber were used to derive the feed forward back propagation neural network and compared to a derived non-linear model. The derived neural network model with fourteen hidden nodes and tansig as transfer function has an R-squared of 0.949 for the training. The comparison shows that ANN has superior predicting capability compared to non-linear modeling even with a limited number of data. Parametric analysis was performed and found that steel fiber shows improvement in the corrosion resistance of concrete for mixtures with low to moderate water-cement ratio and an opposite behavior for high water-cement ratio. This is due to the presence of voids formed around the surface of the steel fiber due to capillary action. These voids serve as highways for chloride ions.

By early 2020, COVID-19 has caused a global pandemic which led to an enormous number of challenges worldwide in various sectors. The Philippine government has implemented multiple quarantine guidelines and travel restrictions to ensure the people’s health and safety. However, the International Labour Organization projected an initial economic and labor market disruption affecting 11 million workers, or about 25% of the Philippine workforce, due to the pandemic. Therefore, the government, thru the concerned agencies continues to encourage employers to implement alternative work plans such as a work-from-home (WFH) operation in compliance with the established regulations in line with existing laws and policies. In line with the telecommuting concept, various research has already been performed, however, some were regarded inconclusive and require further study. Hence, in this study, a Web application was developed along with an embedded fuzzy model to evaluate the telecommuting capability assessment of employees. The proposed web application with embedded fuzzy model is capable of providing capability assessment using the four main input variables which are also relatively characterized for possible telecommuting cost assessment.

Flooding is a perennial problem in the Philippines, particularly in its capital city of Manila. Sampaloc is one of the barangays in Manila vulnerable to flooding according to the Flood Risk Map of Metro Manila. The researchers considered España Boulevard as the area of study since it is one of the most flood prone roads in Manila, according to Metro Manila Development Authority (MMDA). The study is focused on the analysis of Rainwater Gardens as an additional flood mitigation in España Boulevard using Storm Water Management Model (SWMM) simulation. Studies have proven that rainwater garden is considered as one of the most effective, simplest, and low-cost methods to address abrupt flooding. Moreover, it is easy to install, maintain and has a lot of advantages such as removing nutrient-based pollutants such as nitrogen and phosphorus, improving air quality, money saving and water conservation and improving environmental aesthetics. The data was collected through online surveys. The gathered data was calibrated and simulated using SWMM. As per the results, the rainwater garden is effective as an additional flood mitigation system since it can reduce the flood depth up to 19.42% and 14.78% for 25-year return period and 50-year return period storm, respectively. The delay of abrupt flooding is beneficial to the residents of flood prone areas. In real life scenario, the 0.15 m difference in flood depth for a 25-year return period storm and 0.17 m difference in flood depth for a 50-year return period storm will serve as longer time for evacuation of the residents when excessive flooding occurs. Moreover, rescuers will have more time to respond to affected areas and save more people.

Optical character recognition (OCR) is the process of extracting the characters from a digital image. The concept behind OCR is to acquire a text in a video or image formats and extract the characters from that image and present it to the user in an editable format. In this study, a convolutional neural network (CNN) is applied, which is a mathematical representation of the functionality of the human brain, using back-propagation algorithm with test case files of English alphabets and numbers. The purpose of this study is to test systems capable of recognizing vehicle plate number English alphabets and numbers with different fonts, and to be familiar with CNN and digital image processing applied for character recognition. Scientific journals and reports were used to research the relevant information required for the thesis project. The chosen software was then trained and tested with both computer and video output files. The tests revealed that the OCR software can recognize both vehicular plate and computer alphabets and learns to do it better with each iteration. The study shows that although the system needs more training for vehicular plate characters than computerized fonts, and the use of CNN in OCR is of great benefit and allows for quicker and better character recognition.

E-Aid is a study that aims to develop an application based on the convolutional neural network algorithm. The central idea for the creation of E-Aid is to provide a mobile application which offers more advanced capabilities and leads to a strong emergence for the medical health applications in the market. The reliability for the usage of CNN as an algorithm produces positive results which is essential for this study. The researchers trained CNN model that will be used later on during the execution of the CCN algorithm, and this CNN model must be able to identify 4 types of open wounds (laceration, puncture, abrasion and avulsion) and 4 types of skin burns (1st-, 2nd-, 3rd- and 4th-degree burn) and also must be able to classify it whether the wound is infected or not infected. The researchers tested the accuracy of the CNN model before sending to our respondents. The researchers tested the accuracy by getting a random image of open wounds and skin burns in the Internet and run it on the E-Aid app. After the researchers finish testing the accuracy of the app, they distributed the app to their respondents to test furthermore the accuracy and reliability of the app. The researchers’ respondents are composed of 6 medical professionals (doctors/nurses), 5 IT/CS professionals and 14 students (in the field of medicine and computer studies).