With the fast-paced growth of technology in our generation today, a lot of different applications has been developed in order to make people's lives more convenient and comfortable. However, due to the people's attachment to these technology, they have grown more and more sensitive with what they read from the internet (especially social media) — this in return takes toll on their mental health capabilities and provides problems for them and their families. This study aims to promote the presence of a mental health issue that has been frequently discussed by a lot of people through the years — this disorder is known to be depression. The researchers’ aim in this study is to develop a game that can aid people in understanding how depression affects the character through the said game's story — and help alleviate some possible factors that saddens or disrupts the player's mind from the gameplay and music. One of the main elements from this study is that interviews from psychologists, psychiatrists, and guidance counselors would be conducted and analyzed in order to know the possible problems that the target audience of the project may or may not face in their own lives — the researchers would then conduct a survey accompanied with the Patient Health Questionnaire (PHQ-8) in order to test the game's effectiveness and enjoyment. Based from the overall results of the study, the researchers were able to analyze the gathered PHQ-8 survey and has successfully rejected their null hypothesis — which means that the project has an effect on decreasing the severity of depression of the players. Overall, the researchers were able to satisfy their objectives which makes the project a step closer in developing an application that can be positively used for both entertainment and depression awareness in the future.

Attaining high retention rates among engineering institutions is a predominant is-sue. A significant portion of engineering students face challenges of retention. Academic advising was implemented to resolve the issue. Decision support sys-tems were developed to support the endeavor. Machine learning have been inte-grated among such systems in predicting student performance accurately. Most works, however, rely on a black box model approach. Rule induction generates simpler if-then rules, exhibiting clearer understanding. As most research works considered attributes for positive academic performance, there is the need to con-sider ‘negative’ attributes. ‘Negative’ attributes are critical indicators to possibility of failure. This work applied rule induction techniques for course grade predic-tion using ‘negative’ attributes. The dataset is the academic performance of 48 mechanical engineering students taking a machine design course. Students’ at-tributes on workload, course repetition, and incurred absences are the predictors. This work implemented two rule induction techniques, rough set theory (RST) and adaptive neuro fuzzy inference system (FIS). Both models attained a classifi-cation accuracy of 70.83% with better performance for course grades of ‘Pass’ and ‘High’. RST generated 16 crisp rules while ANFIS generated 27 fuzzy rules, yielding significant insights. Results of this study can be used for comparative analysis of student traits between institutions. The illustrated framework can be used in formulating linguistic rules of other institutions.

In the Philippines, there is still a barrier language between the deaf and hard of hearing and abled communities. This could be attributed to how time and resource-consuming it is to learn FSL. This study aims to develop an FSL emergency sign detection in hopes to diminish the prejudice against dhh and the barrier that prevent them from being entirely accepted by non-dhh. To test the hypothesis of how fast hand detection works in emergencies using mobile, YOLO v4 tiny was used as the main algorithm for FSL detection for selected phrases often used in emergencies backed by an online survey for its usefulness. The results yielded an average accuracy of 85% in detecting the hand signs in different background environments with ample lightning and that using the mobile application to detect emergency hand signs is fast and accurate enough when in different backgrounds.

Supervision regarding the wellness and security of the students of NBCA International School is insufficient. As for short break times and only one concessionaire, negligence in the school’s canteen is prominent. To administer and enrich the wellbeing of each student, MoniPay intends to monitor the consumption and expenditures of food purchased using NFC or Near-Field Communication cards where parents are notified of the student’s procuring activities. Reports of student's daily and weekly expenditures are accessible in the parent application. This study emphasizes the need for the system to fulfill what is lacking in administering the wellness of the students in the school. This study is quantitative research wherein data is collected by way of surveys answered by respondents chosen through a purposive sampling technique to improve the system. The results and objectives have been met in line with the gathered information from research, interview, and surveys.

This research exhibits the current state of the groundwater resources of the Province of Marinduque more than 20 years after the mining disaster. The sampling locations included thirty – five (35) sites that were extending all six municipalities of the province. The concentration of chromium, iron, manganese, lead, and zinc exceeded the maximum admissible limit (MAL) based on the Philippine National Standards for Drinking Water (PNSDW) 2017. Thirteen of the sampling sites were classified as severe pollution based on its pollution index. The highest pollution indices were found to be at Brgy. Sumangga, a riverside barangay in the Municipality of Mogpog. These indices were utilized to produce a spatial metal concentration map of the Province of Marinduque using the Empirical Bayesian Kriging (EBK) method. Based on the map, the groundwater of the municipality of Torrijos needs prompt attention for remediation. The findings revealed that the province of Marinduque's groundwater quality is in danger of deteriorating. It is possible to infer that EBK is an effective method for monitoring groundwater quality based on the data and correlation provided. The results of this study could assist in planning rapid response and strategies that are beneficial in the execution of programs that will enhance the adaptive capacity of the province.

Corrosion of steel reinforcement due to hostile environments is regarded as one vital structural health concerns in concrete structures. Specifically, the development of corrosion affects the necessary bond strength of rebar in concrete contributing to the loss of resilience and possible structural failures. It is thus essential to understand the effects of corrosion on bond strength so that remedial measures can be done on existing and deteriorating RC structures. Hence, this study investigated through laboratory experiments and Artificial Neural Network (ANN) modeling the effects of corrosion on bond strength. Experimental results showed that at small amounts of corrosion less than 0.27%, the bond strength was observed to increase. At these levels, the amounts of corrosion products were sufficient enough to expand freely through the permeable structure of concrete and occupy the pore spaces. Beyond this level, however, the bond strength of concrete deteriorated significantly. There was an observed average decrease of 1.391 MPa in the bond strength values for every percent increase in the amount of corrosion. The expansive and progressive internal radial stress due to corrosion resulted to the development of internal and surface cracks in concrete. In the parametric investigation of the derived ANN model, the bond strength was also observed to decline continuously with the growth of corrosion derivatives as represented by the relative magnitudes of the ultrasonic pulse velocity (UPV). The prediction results of the model can be utilized as basis for design and select appropriate mitigating measures to prolong the service life of concrete structures.

Ultra-High Strength Concrete (UHSC) is known for its brittleness compared to traditional concrete, which can lead to sudden collapses. When it comes to columns, failures are particularly serious and require the use of confinement models to accurately predict the strength and strain of confined UHSC columns. While previous confinement models exist, many equations either underestimate or overestimate the confinement of concrete due to idealized assumptions and the exclusion of significant variables. This study employs a hybrid machine learning approach to capture the complex interactions in confinement behavior and accommodate a broader range of peak strength and axial strain parameters in UHSC. Statistical performance measures indicate the superiority of the proposed models over existing equations. Through causal inference, the study assesses the effects and relative importance of each parameter on peak strength and axial strain. The visualizations provided by the performance plots helped identify patterns and correlations that would have been difficult to discern through numerical analysis alone. The developed NN-PSO models are proven effective in reasonably predicting the peak strength and axial strain of UHSC columns.

Data analysis is an integral part of research. Most researchers examine their results by using graphs, tables, charts, and figures. These methods are effective, but knowledge transfer is limited because it only depends on what the authors or researchers have presented. The need to scrutinise further the given data is essential. One way of addressing this problem is to utilise a graphical user interface (GUI), wherein a user can manually choose some parameters of an extensive dataset to display and analyse. In this paper, the results of the four variants of clustering techniques, namely the Ant Colony Optimization (ACO), Gaussian Mixture Model (GMM), K-Power Means (KPM), and Kernel-Power Density-Based Estimation (KPD), in grouping the wireless multipath propagations, are evaluated through the use of a GUI. The accuracy performance of each clustering algorithm can be obtained by choosing in the GUI the corresponding channel scenario that the user would like to investigate. A deeper analysis of the clustering characteristics can also be done by selecting other parameters in the GUI. This selection gives a better understanding of the behaviour of each clustering technique and provides an effective way of presenting and analysing the different sets of data.

SEEP/W is a sophisticated finite element program for simulating groundwater flow in porous media. SEEP/W can also model simple saturated steady-state problems as well as complicated saturated/unsaturated transient analysis. This study aims to determine the effects of the different types of isotropic soils on the seepage and exit gradient of homogeneous earth dams through using seepage analysis simulation. A total of sixty simulations were performed to determine the effects of each six different types of isotropic soil, including gravel, silt, silty sand, silty clay, clay and sand, on the seepage and exit gradient of homogeneous earth dams. In the simulation data, the highest value of the exit gradient is from gravel, which is 0.50003672, while the lowest value is from clay, with a value of 0.500029. In seepage flux, gravel and clay soil have the highest and lowest values, with a minimum and maximum value of 3.00E−04m3/s and 3.00E−02m3/s for gravel and 1.00E−11m3/s and 4.70E−09m3/s for clay,, respectively. The computed r-value is 0.623 and the tabular value is 0.2546766 with 58 degrees of freedom and a 0.05 level of significance. Due to the r-value of 0.623, which is in between the r-values of 1.0 and 0.5, the seepage and exit gradient have a Positive Relationship in terms of Pearson's Correlation Method. Based on this data, it is highly recommended to use clay soil for designing embankment dams as it has low value for both seepage and exit gradient which could prevent piping. While gravel should be avoided among the six types of soil that were simulated since it has a high value for both seepage and exit gradient, making it more susceptible to piping which is one of the major causes of dam failure.

Personal financial management is undeniably a worthwhile practice to establish a financial security during a struggling economy and make intelligent monetary decisions regardless of the plethora of spending temptations. Monitoring personal cash flow is part of achieving financial independence, and it is now undemanding to perform because of the available personal budget apps and finance tools. Nevertheless, a missing feature of these technology-driven innovations is the recording, tracking, and monitoring of receipts as well as the generation of personal expenses reports based on these collected pieces of papers. With this application, “Mobile Bookkeeper”, financial enthusiasts can just scan the receipt using the inbuilt camera of any smartphone and details will be automatically transcribed using Optical Character Recognition (OCR). To measure the satisfaction and test the usability of the mobile app, subjective and objective measures via ISO 25062 and ISO 9241 standards were collected, and QUIS 7.0 questionnaire, respectively. The testing results established Mobile Bookkeeper particularly on its receipt scanner feature as a needed mobile finance app. Together with this acceptance is the report highlighting issues and challenges in developing such mobile application especially with OCR integration and its accuracy in text recognition.