Online-based technology in relation to the investment system for poultry and swine in the Philippines is not well known. Thus, study aimed to develop a Web-based Investment System that would help investors look for the best poultry and/or swine farm which could generate better investment returns. An Investor – Farmer matching system with decision process was developed using a user-friendly online interface for Pork Producers Federation of the Philippines Incorporated (formerly National Federation of Hog Raisers Inc. or NFHFI). This will contain a dashboard on the system administrator side that would reflect the overall performance and financial standing of participating livestock farms as well as comprehensive dashboard for investors which would display the historical and current investment data of every investor account. Main functions of the system include account management, enhanced decision making, maximizing the profit through Return on Investment analysis based on farmers’ production data and profile. One-on-one interviews, focused groups and direct observations will be employed to collect primary data required for the system development. The effectiveness of the system and client satisfaction were discussed and presented after months of deployment in PPFPI website.

This project is to make a decision based Virtual Reality Film that focuses on leadership, and a website for the client. The purpose is to help and support Kaya Natin Youth Leadership Academy in improving leadership qualities, critical thinking skills and their understandings of the teachings in the academy. By creating this interactive film, the proponents would like to let the viewers grasp the purpose of good leadership and how it would help their view as a leader. The film will be created using multiple software for animating and developing the VR set up, while the website will be created using coding editors and web designing software. This project will improve the proponents’ skills in animation, editing, web designing, and VR developing. The creation of this project and website will help in support for the improvement of the youth's understanding of the importance of leadership and its purpose in life.

With the consent of Bureau of Jail Management and Penology (BJMP), this project aims to provide a more efficient method of communication between inmates and their visitors and an improved scheduling system for online visitation. The project focuses on creating an application that will allow inmates and relatives to communicate via e-mail and video call. However, inmates and relatives must agree to the policy that their conversations will be under the surveillance of BJMP for security purposes. In order to develop the system, the developers used programming languages like PHP, SQL, and Java. The development of the system was done through scrum methodology since this is the most suitable approach for the developers. After developing the system, the developers were able to see how their system functions. Data regarding the functionality, usability, reliability, performance, and security of the system was gathered using questionnaires from 50 respondents who were composed of six BJMP officers, 24 FEU Tech students, and 20 people who are related with someone inside a jail. The developers learned that the system can still be improved in terms of reliability and usability because these are the factors that gathered an average score in their tally of answers from respondents. The developers recommend to future researchers that are going to tackle this kind of research to focus more on the efficiency of the system. The developers also recommend to provide more security features to further improve the security of the project.

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 intensity of urban flooding area due to rapid urbanization in Metro Manila has been worsening over the years caused by the torrential rains brought by the Southwest Monsoon. To further characterize the impact of land use change influenced by urbanization, we compared the flood map generated from two periods (Year 200 & Year 2020) using a two-dimensional hydrodynamic modelling simulated in FLO-2D software. In our simulations, we assigned roughness coefficient values to corresponding land use category derived from an earlier study in the area previously spearhead by JICA in 2001. Each model will incorporate the implemented Year 2000 land use and the projected Year 2010 land use classification respectively, which were used in this earlier study. Meanwhile, both models will use the same sets of parameters for the simulation: IFSAR-derived DEM elevation model and a rainfall event with 10-yr return period. The area of interest of this study is located near Valenzuela-Obando-Meycauayan (VOM) with its boundaries defined from the National Mapping and Resource Information Authority. The flood simulations conducted do not take into consideration in existing flood control measures such as drainage systems and floodwalls to minimize the complexity of the model. The results are evaluated both quantitatively and qualitatively. According to the results, the impact of the land use change on flood formation in most areas are insignificant due to a low degree of land use change. However, there has been substantial impact on flooding in specific areas where there is a major change in the land use. For further studies, we recommend the use of a longer land use change period and the consideration of more varied and precise Manning’s n-values.

This paper illustrates the impacts of mining disaster after more than 20 years. A two – day rapid assessment was carried out at Mogpog and Boac River catchment in Marinduque Island in March 2019. The target site included Maguilaguila Pit that connects the river catchment and formerly used as mining wastes pit. This is to understand the impacts of 1993 and 1996 mining disasters in the Boac-Mogpog river basin at Marinduque, Philippines. The island of Marinduque has been considered as among the top ten most vulnerable islands in the country due to its environmental condition and geographical location which affected the island demography. The island has suffered the impacts of one of the country’s biggest mining disasters. The instruments used to conduct rapid site assessment were SciAps X-300 Handheld X-ray Fluorescence (XRF), Unmanned Air Vehicle (UAV) Model DJI Mavic Air, Google Earth, Hannah Multiparameter HI 9811-5 with HI 1285-5 probe and HI 70007, 70031, 70032 and 700661 solutions. The DJI Mavic Air captured images of Mogpog and Boac River catchment which helped direct the research team to take the right sampling locations. The DJI Mavic Air captured site images of the two rivers as dead rivers and use as land transportation route during dry season. The Google Earth captured the historical images of the target areas. The recorded data showed that the pit and nearby river water is acidic with pH equivalent to 2.9 and 4.1, respectively. The range of concentration of total dissolved solids in Mogpog and Boac river water was 100–1360 and 160–1150 ppm, respectively. The recorded concentration of iron near the pit was 125,587 ppm, and chromium concentration range was 80–99 ppm. The concentration of copper and manganese in the sediments was 5 and 158 times higher (respectively) than the 1998 detected concentrations. Based on the recorded data and images, the Maguilaguila pit, Boac and Mogpog River catchment need immediate attention. It could be concluded that based on the recent assessment results, leaks at the pit are likely. Also, the combination of areal-aerial and ground technique produced two – day rapid site assessment for areas contaminated by mine tailings. The information could aid in preparing prompt action and setting strategies that are helpful in carrying out risk reduction programs in the island.

Essay writing examination is commonly used learning activity in all levels of education and disciplines. It is advantageous in evaluating the student’s learning outcomes because it gives them the chance to exhibit their knowledge and skills freely. For these reasons, a lot of researchers turned their interest in Automated essay scoring (AES) is one of the most remarkable innovations in text mining using Natural Language Processing and Machine learning algorithms. The purpose of this study is to develop an automated essay scoring that uses ontology and Natural Language Processing. Different learning algorithms showed agreeing prediction outcomes but still regression algorithm with the proper features incorporated with it may produce more accurate essay score. This study aims to increase the accuracy, reliability and validity of the AES by implementing the Gradient ridge regression with the domain ontology and other features. Linear regression, linear lasso regression and ridge regression were also used in conjunction with the different features that was extracted. The different features extracted are the domain concepts, average word length, orthography (spelling mistakes), grammar and sentiment score. The first dataset used is the ASAP dataset from Kaggle website is used to train and test different machine learning algorithms that is consist of linear regression, linear lasso regression, ridge regression and gradient boosting regression together with the different features identified. The second dataset used is the one extracted from the student’s essay exam in Human Computer Interaction course. The results show that the Gradient Boosting Regression has the highest variance and kappa scores. However, we can tell that there are similarities when it comes to performances for Linear, Ridge and Lasso regressions due to the dataset used which is ASAP. Furthermore, the results were evaluated using Cohen Weighted Kappa (CWA) score and compared the agreement between the human raters. The CWA result is 0.659 that can be interpreted as Strong level of agreement between the Human Grader and the automated essay score. Therefore, the proposed AES has 64-81% reliability level.

In this paper, we analyze the effect of the representative spectral indices, normalized difference vegetation index (NDVI), normalized difference water index (NDWI) and normalized difference built-up index (NDBI) on classification accuracies of Landsat-8 OLI image. After creating these spectral index images, we propose five methods to select the spectral index images as classification features together with Landsat-8 OLI bands from 1 to 7. From the experiments we observed that when the spectral index image of NDVI or NDWI is used as one of the classification features together with the Landsat-8 OLI bands from 1 to 7, we can obtain higher overall accuracy and kappa coefficient than the method using only Landsat-8 OLI 7 bands. In contrast, the classification method, which selected only NDBI as classification feature together with Landsat-8 OLI 7 bands did not show the improvement in classification accuracies.

Microalgae offer promising and multifaceted solutions to the ongoing issues regarding energy security and climate change. One of the major bottlenecks in utilizing algal biomass is the excessive amount of moisture to be managed after harvest, which translates to costs in the dewatering step. Newer strategies have been developed to be able to convert algal biomass feedstock to biodiesel without the need for extraction and drying, such as in-situ transesterification. This process can be improved by concurrently subjecting the system under subcritical conditions, which could also potentially remove the use of catalysts as well as offer tolerance to free fatty acid content of the feedstock. A definitive screening design of experiment was utilized to provide an acceptable prediction on the effects of key process parameters – temperature, reaction time, and solvent-to-solid ratio to the obtainable fatty acid methyl ester (FAME) yield and process power consumption. The optimum operating condition, which combines the benefits of maximizing the FAME yield and minimizing the process power consumption was found to be at 220 °C, 2 h, and 8 ml methanol per gram of biomass (80 wt% moisture). This produces a FAME yield of 74.6% with respect to the maximum obtainable FAME. Sensitivity analysis discussed the implications regarding the weight of importance between the two responses of interest. The benefits of the proposed process can be observed when compared to its conventional transesterification counterpart in terms of energy savings and reduced environmental impact. Hence, this process offers a feasible alternative to produce biodiesel from microalgae.

A trigeneration system consists of interdependent technologies for power generation, heat generation, and the generation of cooling effect which leads to the overall improved thermodynamic efficiency of the system. However, the optimal design of a trigeneration system also depends on the product price variability of the energy streams which are highly dependent on the price of the raw materials and the product demand. Taking into consideration expected price fluctuations of streams, it is then possible for plant owners and engineers to evaluate the investment risk associated with the design capacity of a trigeneration system. The study proposes the use of a 2k factorial design of experiments together with fuzzy linear programming to conduct a price sensitivity analysis in the optimal design of a trigeneration system. This type of analysis can provide plant owners information on possible configurations for optimal capacity given uncertainty in the price parameters.