This study determined the items that could predict university students’ susceptibility to disinformation (e.g., fake news). Toward this goal, randomly-selected students from the four private universities in Manila answered a content-validated and pilot-tested survey form. Through binary logistic regression analysis, it was found that frequent visits to Instagram, sharing a political post of a friend, and liking a post of a political party could increase the susceptibility of students to fake news. On the other hand, sharing the post of a political party, and seeking the opinion of experts could decrease the susceptibility of students to fake news. Of these items, liking a post with a similar opinion of a political party – a confirmation bias – had the highest contribution to fake news susceptibility of students. It is worth noting that the most reliable source of information, i.e. the library, is the least utilized fact-checking resource. It can be concluded that technological, internal, and external factors contribute either positively or negatively to the susceptibility of students to fake news. Implications to combat fake news are offered.

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.

Wireless multipath clustering is an important area in channel modeling, and an accurate channel model can lead to a reliable wireless environment. Finding the best technique in clustering wireless multipath is still challenging due to the radio channels’ time-variant characteristics. Several clustering techniques have been developed that offer an improved performance but only consider one or two parameters of the multipath components. This study improved the K-PowerMeans technique by incorporating weights or loads based on the principal component analysis and utilizing the Minkowski distance metric to replace the Euclidean distance. K-PowerMeans is one of the several methods in clustering wireless propagation multipaths and has been widely studied. This improved clustering technique was applied to the indoor datasets generated from the COST 2100 channel Model and considered the multipath components’ angular domains and their delay. The Jaccard index was used to determine the new method’s accuracy performance. The results showed a significant improvement in the clustering of the developed algorithm than the standard K-PowerMeans. 

Torrefaction is a thermochemical process used to convert the biomass into solid fuel. In this study, torrefaction increased the raw microalgal biomass’ energy content from 20.22 MJ⋅kg−1 to 27.93 MJ⋅kg−1. To determine if more energy is produced than energy consumption from torrefaction, this study identified the energy balance of torrefied microalgal biomass production based on a life cycle approach. The energy analysis showed that, among all processes, torrefaction had the least amount of energy demand. The experimental setup, defined as scenario A, revealed that the principal source of energy demand, about 85%, was consumed on the microalgal growth using a photobioreactor system. A sensitivity analysis was also performed to determine the varying energy demand for torrefied microalgal biomass production. The different types of cultivation methods and various production scales were considered in scenarios B to D. Scenario D, which represented the commercial production-scale, the energy demand drastically decreased by 59.46% as compared to the experimental setup (scenario A). The open-pond cultivation system resulted in the least energy requirement, regardless of the production scale (scenarios B and C) among all the given scenarios. Unlike scenarios A and D, scenarios B and C identified the drying process to consume a high amount of energy. All the scenarios have shown an energy demand deficit. Therefore, efforts to decrease the energy demand on the upstream processes are needed to make the torrefied microalgal biomass a viable alternative energy source.

Computer programming education is often delivered using individual learning strategies leaving group learning techniques as an under-researched pedagogy. This pose a research gap since novice programmers tend to form their own group discussions after lecture meetings and laboratory activities, and often rely on peers when a topic or activity is difficult. Thus, this study intends to evaluate the impact of cooperative learning using jigsaw technique when teaching computer programming to novice programmers. A quasi-experimental research using a nonequivalent control group pretest-posttest design was adopted to examine the impact of jigsaw teaching strategy. After a 14-week programming course, pre- and post-test results revealed a significant increase in terms of attitude and self-efficacy, and the experimental group demonstrated significantly higher scores than in the control group. Therefore, it was concluded that cooperative learning using Jigsaw technique is a valid and effective teaching strategy when handling novice programmers in an introductory programming course.

Flood is imminent when heavy rain occurs, identifying the level of water in plain sight is difficult to achieve. There are currently available ways to detect flood water but usually are very expensive and needs a huge equipment with sensors. The research has proposed an alternative solution to expensive ways on detecting flood and water levels. The study created an application to detect body of water by using image processing technique called Region-based segmentation algorithm to detect water on the image and Canny Edge Detection with computation using Pixel Ratio on a selected water region to determine the height of the water or flood. A CCTV camera was used to capture the image and was fed on the application through the network infrastructure. Once captured, the image was processed to detect the body of water and measurement of its level. The testing of the application was done on a controlled environment and the application was able to detect the water body on the picture. It was able to detect the edge of the water based on a selected region where the water is found. The measurement of the actual height of the water, closely matches the height of stated in the application. Thus, the research has found a way to detect body of water and gauge its water level using image processing, in which, have found a way to detect and measure water affordably. This research can be a step, in future research like monitoring the streets’ flood level when heavy rains occurs. This is a much more safe and affordable way to monitoring the increase and decrease of flood.

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.

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.