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.

The shift to a circular economy requires careful planning, the first step of which is to understand the drivers of the transition. There have been few papers in the literature that have analyzed and mapped interrelationships of these transition drivers from the perspective of different sectors. This work presents a methodological framework for mapping causality networks for macro-level transition towards circular economy based on sector perceptions. Fuzzy DEMATEL is used to allow linguistic inputs to be quantified. This procedure allows drivers to be characterized as causes or effects based on their position in the causality network. A case study presents the Philippines as a representative developing country for circular economy transition. The inputs of seventeen respondents from retail and trade, manufacturing, construction, water services, food services, electricity services, academic services, and health services were elicited through a survey. These responses were then aggregated into the industry and service sectors. The drivers considered were government support, company culture, consumer demand, social recognition, economic attractiveness, and information to practitioners. Results show that economic attractiveness and consumer demand are unanimously seen as the causal drivers. All sectors identify company culture as an effect driver. The findings also indicate varying perceptions among sectors. Although these findings apply specifically to the Philippines, this methodology itself can be used for mapping driver interrelationships of other countries and regions.

From the outbreak of a novel COronaVIrus Disease (COVID-19) in Wuhan to the first COVID-19 case in the Philippines, Filipinos have been enthusiastically engaging on Twitter to convey their sentiments. As such, this paper aims to identify the public opinion of Filipino twitter users concerning COVID-19 in three different timelines. Toward this goal, a total of 65,396 tweets related to COVID-19 were sent to data analysis using R Statistical Software. Results show that “mask”, “health”, “lockdown”, “outbreak”, “test”, “kit”, “university”, “alcohol”, and “suspension” were some of the most frequently occurring words in the tweets. The study further investigates Filipinos’ emotions regarding COVID-19 by calculating text polarity of the dataset. To date, this is the first paper to perform sentiment analysis on tweets pertaining to COVID-19 not only in the Filipino context but worldwide as well.

According to National Center for Education Statistics, almost half of the first-time freshmen full time students who began seeking a bachelor’s degree do not graduate. The imbalance between the student enrolment and student graduation can be solved by early predicting and identifying students who are prone of not having graduation on time, so proper remediation and retention policies can be formulated and implemented by institutions. The study focused on the application of the ensemble models in predicting student graduation. Ensemble modeling is the process of running two or more related but different analytical models and then synthesizing the results into a single score or spread in order to improve the accuracy of predictive analytics and data mining applications. The study recorded an increase of classification accuracy in predicting student graduation using ensemble models and combining multiple algorithms.

Computational and statistical tools help manage the prevailing challenges of the 17 Sustainable Development Goals (SDGs) by providing meticulous understanding of contemporary issues. However, complex challenges are difficult to handle with conventional techniques, resulting to the need for more advanced methods. Artificial neural networks (ANNs) are often used as an advanced approach in modelling complex behaviour of systems. Evaluating the current utilization of ANNs helps researchers gauge their applicability to SDG-related issues. The gaps among the studied SDGs need to be addressed through a comprehensive survey of the state-of-the-art literature. Hence, this work reviews published journal articles on the application of ANNs in resolving issues of the SDGs. This review identifies the current trends and limitations of ANN for SDG, and discusses its prominent applications and field of utilization. Descriptive and content analysis of journal articles is performed for this review. Journal articles from the Scopus database reveal Clean Water and Sanitation, Affordable and Clean Energy, Sustainable Cities and Communities, and Responsible Consumption and Production are the most popular subject matter for modelling and forecasting. New innovative functions include feature selection, kriging, and simulation. The main contribution of this work is a comprehensive mapping of the current state of this area of research. This work aims to aid future researchers to recognize further possible uses of ANNs with respect to the SDGs.

History education ordinarily faces a relativist slant, if not by the monotonous nature of the course. Hence, educators are continuously in pursuit for a better teaching strategy to keep the class interesting. The main goal of this study was to 'bring history to life' through a mobile application powered by augmented reality that can provide an immersive storytelling experience on the American colonisation period in the Philippines. Outlined as a second quarter lesson in K to 12 Basic Education Curriculum for Social Studies by the Department of Education, the historic augmented reality application (HARA) depicts the: 1) Battle of Manila Bay; 2) Mock Battle of Manila; 3) First Shot in Philippine-American War. Through a participatory approach, co-designers evaluated HARA in terms of pedagogical value (knowledge acquisition, acceptability, motivation, and attitude), and quality standards (effectiveness, satisfaction, and efficiency) through a usability inspection method. The iterative nature of the project development via a co-design approach with end users revealed early bugs, shortcomings, and possible improvements on the app. The rest is history!

Because of its potent force within the educational settings, digital game-based learning is considered as a methodological means to keep up with the changing pedagogical landscape precipitated by technological developments. While educational technology has already established its presence in most parts of the education sector, it is still under-used in kindergarten level. The purpose of this study is to develop a digital educational game called “Kinder Learns” by using visual novels as the game design and K to 12 Kindergarten Curriculum Guide of the Department of Education as the game content, and to investigate its impact as an educational tool for preschoolers and educators. A digital educational game development methodology was employed to sensibly plan, design, develop, and implement Kinder Learns. The game impact was then evaluated by 243 preschoolers and 29 preschool teachers from the Pasay Schools Division of the Department of Education using the Serious Game framework, which is composed of learning and pedagogy theory in combination with gaming requirements. The result supports the acceptance of the game as an educational tool for knowledge enhancement in preschool. Kinder Learns shaped a novel realization that technology has a place in the early education landscape. Consequently, the game has successfully reinforced the positive influence of employing a digital educational game into the classroom curricula of preschool education and the unequivocal stakeholders’ perceptions towards technology use in early education years.

The composite action of reinforcement in the surrounding concrete involve a complex and non-linear mechanism.Inadequate understanding of the underlying interactions may lead to designs with insufficient amount of bond resistance of reinforcing bars in concrete structures.To investigate the effects of various parameters on the bond strength of steel bars in concrete, 54 cube samples with varying embedded reinforcements and strengths were prepared. The samples were cured for 28 days and tested using ultrasonic pulse velocity (UPV) test for sample homogeneity and single pull out test for bond strength.Data gathered in the experiment were used in the development of bond strength model as a function of compressive strength, concrete cover to rebar diameter ratio, embedment length, and UPV using artificial neural network (ANN). Of all the bond strength models considered from various literatures, the neural network model provided the most satisfactory prediction results in good agreement with the bond strength values obtained from the experiment. The UPV parameter was found to be one of the most significant predictors in the neural network model having a relative importance of 20.57%. This suggest that the robust prediction performance of the bond model was attributed to this essential component of the model. The proposed model of this study can be used as baseline information and rapid non-destructive assessment for zone wise strengthening in reinforced concrete.

One of the most significant innovations on the workability of concrete that was achieved in recent years is self-compacting concrete (SCC). This desirable performance can be attained through the addition of admixtures to enhance its properties. In this study, superplasticizers were blended with fly ash and air entraining admixtures and were tested for Slump Flow, V-Funnel, L-Box, U-Box, and Screen Stability tests based on the European Federation of National Associations Representing for Concrete (EFNARC) specifications and guidelines for SSC. Based on the results of the study, Fly ash with spherical smooth texture enhances the lubrication between the concrete particles while the air-entrainer provides microscopic bubbles acting as ball bearings between aggregates. The best result was obtained in the specimens containing 5.0% superplasticizers due to its dispersibility effect and reduced flow resistance. In general, the air entraining agent blended with 3.7% superplasticizer exhibited the best performance in all workability test conducted.

An essential component in decision making for site planners is the availability of risk maps to various geological hazards. Liquefaction in particular can be devastating and impose disastrous damage to existing structures built in earthquake prone areas like the province of Mindoro. Through the aid of in situ data, a simplified method of evaluating earthquake induced liquefaction potential was carried out in this study. This is to address the difficulty and high cost necessary to carry out the development of a liquefaction risk map. Borehole data were collected from different locations in Mindoro and the earthquake liquefaction severity index in each location were calculated using deterministic approach. Results showed that different levels of liquefaction severity were obtained in various areas of Mindoro. There were locations exhibiting manifestations of surface liquefaction due to 7.1 Mw earthquake with a peak ground acceleration of 0.4g. The generated liquefaction severity maps can be utilized as baseline information in selecting appropriate geotechnical intervention for soil improvement and stabilization. Further, the indices can be used as additional dimension of evaluating the holistic reliability of existing engineering structures.