A Sound-based Machine Learning to Predict Traffic Vehicle Density

Traffic flow mismanagement is a significant challenge in all countries especially in crowded cities. An alternative solution is to utilize smart technologies to predict traffic flow. In this study, frequency spectrum describing traffic sound characteristics is used as an indicator to predict the next five-minute vehicle density. Sound frequency and vehicle intensity are collected during a thirteen-hour data gathering. The collected sound intensity and frequency are then used to learn three machine-learning models - support vector machine, artificial neural network, and random forest and to predict vehicle intensity. It was found out that the performances of the three models based on root-mean-square-error values are 12.97, 16.01, and 10.67, respectively. These initial and satisfactory results pave a new way to predict traffic flow based on traffic sound characteristics which may serve as a better alternative to conventional features.

The Market Distortion Effect of Government Intervention in Higher Education

In the Philippines, a mixture of public and private higher education institutions (HEIs) exists. Because of the government subsidy among the public HEIs, the major concern of the private sector is the “uneven playing field”. This study hopes to eliminate this unhealthy competition and market distortion in the educational landscape. This study utilizes the quantitative non-experimental retrospective explanatory design. Results show that regardless of the extent of state intervention, the market can be distorted. Hence, assigning specific programs to be offered based on the type of HEI is a good alternative. The programs to be fully subsidized and offered by the government are agriculture, sciences, engineering, manufacturing and construction, and humanities and arts (HA) while services, health and welfare, education and social sciences, business and law programs will be for private sector service providers. This stimulates healthy competition among the HEIs leading to a better quality of education.

A Computer Vision Application for Measuring the Deflection in a Two-dimensional View of Reinforced Concrete Beams

A novel computer vision application is developed to measure the deflection of two-dimensional (2D) reinforced concrete structural members. Eight beam samples, with dimensions of 160 mm x 150 mm x 1400 mm are loaded and simulated under a four-point loading test until failure using a reaction framework machine. A camera is fixed at the center front view of the concrete beams to capture the deflection of the samples while testing. In each test, a dial indicator is installed and the maximum deflection is manually recorded. Based on the results, the maximum deflection values recorded by the proposed application obtained an average error of 18.38 % when compared to the manual measured results. This indicates that computer vision-based application can provide a beam-wide scale deflection performance, compared to the traditional point-based deflection reading. This study paves a new possibility of aiding manual measurements of concrete beams and all other structural studies.

Remote Learning Readiness and Challenges: Perceptions and Experiences among Tertiary State University Management Students

COVID-19 has disrupted the education system globally, leading education institutions to migrate into remote learning. This study on online learning readiness and competence was conducted among management students on their perceptions on the importance of, and the confidence level of their online learning competence factors. Using the Student Readiness for Online Learning (SROL) instrument, the results show that the students consider Technical Competence as very important and that they are somewhat confident with their online learning competence. Both perceptions of the importance of the online competence factors and competency levels significantly correlate with the students’ self-report of whether or not they have learned in the course. Among the eight online learning challenges, the students find the “lack of technical skills in using online learning” as the least challenging. This study concludes with the recommendations that pedagogical and technological interventions be pursued to address the inadequacies in the online teaching-learning process.

Utilization of Wastewater from Different Sources as Potential Electrical Energy Source

This study is about the generated electricity from wastewater using carbon electrodes with the absence/presence of salt bridge through the biofilm anode of the microbial fuel cell (MFC) technology. The three wastewater samples used were from a pond, an abaca pulp mill, and rice fields. Results showed that one of the abaca pulp mill treatments, using carbon rod electrodes with salt bridge presence, reached the highest mean voltage and current to 578.7 mV and 0.2022 mV, respectively. The study revealed that the number of sensible power generation days, from start to end of experimentation, has a significant difference between treatments. Throughout the 20-day fermentation process, a total count of 8.94x105 colony forming units (CFU) per ml was found and purified from the biofilm anodes from pond wastewater. Likewise, 9.14x105 CFU per ml isolates from the abaca pulp mill, and 1.65x106 CFU per ml isolates from the rice field.

Performance Evaluation of a Rice Hull-fueled Cabinet Food Dryer

This study was conducted to determine the technical performance of a rice hull-fueled cabinet food dryer. Data gathered from the drying performance in terms of drying behavior, drying rate, and dryer heat utilization efficiency using cassava chips was analyzed through an experimental design. The study also evaluated fuel consumption, thermal conversion efficiency, energy use efficiency, heat utilization efficiency, temperature distribution and economic performance. Results indicated that the dryer's average fuel consumption and the thermal conversion efficiency is 11.19 kg/hr and 6.17%. While the energy use efficiency is 41.51%, and the dryer efficiency is 11.79 %. The moisture reduction of the rice hull-fueled dryer is 8.156 kg/hr, and the heat utilization efficiency is 41.51%. The break-even of using the dryer is ₱ 4.988/kg; if rented out at ₱ 59.86/hr, the payback period is 0.988 batch/hr.

Extended Technology Acceptance Model to Examine the Use of Google Forms – based Lesson Playlist in Online Distance Learning

Shifting to online distance learning due to the COVID-19 pandemic challenged educators' roles as instructional materials designers. This study aimed to examine the students' acceptance of the teacher-designed e-learning tool called Google Forms-based Lesson Playlist (GF-LP) in a home-based online distance learning environment. This quantitative research analyzed 570 responses from Grades 11 and 12 students at a private school in the Philippines using the partial least squares-structural equation modeling. Results showed that perceived self-efficacy and system quality strongly affect the users' perceived ease of use while perceived ease of use highly influenced the students' perceived usefulness of GF-LP. Facilitating conditions do not affect the users' attitudes towards using the e-learning tool, which confirmed the effective utilization of GF-LP in online distance learning. The relationships between the original constructs of the Technology Acceptance Model (TAM) were also presented. This study recommends the use of GF-LP or its features for remote learning.

Mortality Analysis of Early COVID-19 Cases in the Philippines Based on Observed Demographic and Clinical Characteristics

This study aims to determine the demographic, epidemiologic, and clinical characteristics of COVID-19 cases that are highly susceptible to COVID-19 infection, with longer hospitalization and at higher risk of mortality and to provide insights that may be useful to assess the vaccination priority program and allocate hospital resources. Methods that were used include descriptive statistics, nonparametric analysis, and survival analysis. Results of the study reveal that women are more susceptible to infection while men are at risk of longer hospitalization and higher mortality. Significant risk factors to COVID-19 mortality are older age, male sex, difficulty breathing, and comorbidities like hypertension and diabetes. Patients with these combined symptoms should be considered for admission to the COVID-19 facility for proper management and care. Also, there is a significant delay in the testing and diagnosis of those who died, implying that timeliness in the testing and diagnosis of patients is crucial in patient survival.

Factors Affecting Teachers' Resiliency Amidst the COVID-19 Pandemic

Despite the threat of the COVID-19 pandemic, teachers continue to navigate the new normal in education which demonstrate their resilience. This descriptive correlational study determined the level of optimism, job stress, and emotional exhaustion of 150 teachers amidst the COVID-19 pandemic, and it analyzed the relationship among these three variables. The data were analyzed using weighted mean, standard deviation, and Pearson r. Results revealed resiliency among teachers amidst the pandemic with a high level of optimism and a relatively low COVID-19 induced job stress and emotional exhaustion level. This study has also shown that optimism is not correlated with job stress and emotional exhaustion, but it found out significant relationship between the last two variables. Generally, it can be concluded that teachers are highly optimistic amidst the pandemic. This paper also validates earlier claims that lower levels of job stress contribute to lower incidents of emotional exhaustion.

Sequential Pattern Mining of Tourist Spatiotemporal Movement

The study aimed to develop a software application to capture tourist activity information, extract movement patterns from the dataset through sequential pattern mining (SPM), and visualize spatiotemporal movement. Tourist activity information was captured through crowdsourced trajectory movements by scanning unique QR (Quick Response) codes for each visited tourist spots. The AprioriAll algorithm was used to find frequent trajectory patterns on tourist visits. The resulting maximal k-sequences and their subsequences represent the recommended trip itinerary. The spatial and temporal movements were visualized through a flow map and a heat map, respectively. The directed edges in the flow map show the recommended sequence of tourist sites to visit. The heat map shows the density of tourist visits in different areas at time intervals. The application was validated with selected tour planning experts to verify functional suitability, usability, and acceptability. Experimental results show positive indicators that the application met the users’ expectations.