This research works focuses on the co-pyrolysis of coconut coir fiber combined with PET in order to increase its heating value, in addition to solid mass reduction for prolonged shell life and storage issues. Co-pyrolysis is a process of efficiently producing high-quality biofuel from two or more materials. Parameters combinations were identified using the Taguchi optimization methodology model in MINITAB19. Nine samples with three replications were evaluated. Results revealed that changing the temperature, duration, and feedstock blends show a significant effect on solid mass yield and heating value. The biochar with 75:25 (coconut coir fiber: PET) shows that duration and temperature directly affect the solid yield. For biochar, with 25:75 (coconut coir fiber: PET), pyrolysis duration contributed largely to the output. The highest solid mass reduction with an average of 55% solid yield was obtained. Despite a high solid mass reduction, the heating value measured is only 13 MJ/kg. Feedstock blend with PET to coconut coir ratios of 75:25, 25:75, and 50:50 resulted to an average solid yield of 70%, 65%, and 83% respectively. In terms of heating value, for all three replications, the biochar sample subjected to 200°C, 30 minutes, and PET to coconut coir ratio of 75:25, with an average solid yield of 67%, had the highest value with 20.94 MJ/kg, 24.42 MJ/kg, and 23.55 MJ/kg for Trial A, B, and C, respectively. The result shows that the incorporation of PET effectively increases the heating value of the coconut coir fiber from 10 MJ/kg to 24.42 MJ/kg.

The college entrance examination is vital for program admission. Typically, entrance examinations are conducted onsite using paper and pens. When the COVID-19 pandemic hit, the entrance examination was lifted and physical gatherings were prohibited. Since many schools cannot offer an online admissions exam, they rely on grades and interviews to admit and qualify students for degree programs. However, academic standards differ between schools, and grades may not be enough to assess students' capacity. Thus, this study aims to develop an Online Proctored Entrance Examination System (OPEES) with Degree Program Recommender for colleges and universities to help institutions administer onsite or online entrance tests and generate course suggestions using a rulebased algorithm. The study employed the scrum methodology in software development. OPEES allows applicants to submit applications online, and institutions can manage user accounts, tailor exams and degree programs’ criteria, manage exam dates, and assign proctors. Online proctoring using Jitsi, an opensource multiplatform voice, video, and instant messaging tool with end-to-end encryption, ensures exam integrity. The system’s features were evaluated by 102 respondents, comprised of end-users (students and school personnel) and IT professionals, using the FURPS (Functionality, Usability, Reliability, Performance, and Supportability) software quality model. In the software evaluation, the overall system proved to be functional as perceived by the respondents, as manifested by the mean rating of 4.61. In conclusion, the system's architecture was deemed feasible and offers a better way to streamline admission examinations and determine a student’s applicable degree program by enabling institutions to customize their exams and degree program requirements. It will be beneficial to look into recommendation system algorithms and historical enrollment data to improve the system’s use case.

The daily maximum multi-annual series including the rainfall frequency analysis, are one of the inputs for the design process for stormwater management, that entails numerous procedures: (a) rainfall data gathering from Philippine Atmospheric Geophysical and Astronomical Services Administration (PAGASA), (b) information gathering, and (c) checking all received datasets for missing or different data. To address these setbacks, 6 rain gauge stations located in Region VIII, Visayas, Philippines were used to first determine whether or not the Gumbel Extreme Value (GEV) was the better suitable method to use in producing Rainfall Intensity Duration Frequency (RIDF) than Log-Pearson Type III (LP3) by performing Chi-square test; secondly, to select the better RIDF values; and lastly, the isohyetal maps should be developed for return periods of 2, 5, 10, 25, 50, and 100 years. GEV was a better fit for the x2 values (27.96, 54.59, 52.82, 87.96, 11.78, 7.66) obtained through chi-square test were close to or smaller than the critical value of 30.144. The RIDFs produced in GEV were used in plotting isohyetal maps. In all return periods, Borongan generated the highest rainfall intensity value.

The electro-mechanical salted egg grading system was developed to support producers by streamlining the cleaning process, delivering a sorted outcome, saving time, decrease human resources needs, labor costs, and minimized egg breakage, consequently boosting production efficiency. OpenCV (Open Source Computer Vision Library) was employed as a development platform and the Raspberry Pi 3 Model B as a microcomputer due to its speedier and more powerful CPU, which is required to operate the system's components and process the acquired images for classification. In addition, a Raspberry Pi camera module V2 was employed to capture the images for scanning, LED bulb for candling, and an SG90 micro servo for sorting. Furthermore, we used B66 and B35 V-belts for the conveyor assembly. An induction motor of 0.125 horse power is used to rotate the conveyor assembly, a chain, and sprocket to reduce its speed. The researchers also used soft bristles brushes which are ideal for cleaning the eggshell. For cleansing, sprinklers were used along with the water PVC pipe that holds pressurized water of 30 psi. The camera's captured images are categorized as clean, dirty, well-pickled, and spoilt eggs. Empirical results exhibited that the detection accuracy achieved 96% and 93% for cleanliness and quality, respectively. It establishes the model and prototype's robustness in cleaning, sorting, and grading salted eggs.

Oclean's goal is to raise awareness about the harmful effects of plastic waste in the ocean. The game was created for use on a mobile device. It's a 2D game with a single player endless mode. A scoring system and a timer were also included in the game. In addition, the game has a gallery where players could see what is happening in real life. This initiative believes that little acts can generate significant effects in the fight against plastic pollution in our oceans. In addition, interactive information about the issues of marine plastic trash was also provided. The game's set of objectives were completed, and an unending casual game about ocean cleaning and marine plastic garbage was created effectively.

Grit plays a crucial role in determining high individual success more than intellectual talent alone. However, there is no existing literature that ventured into the trait identification in an e-learning environment. This study presents a comprehensive computational-driven strategy for detecting a learner's grit using machine learning. Empirical results show that DBSCAN and Random Forest models produce average accurate prediction consistency of 92.67% against the questionnaire method. Knowledge interpretation using feature importance and association mining quantifies perseverance and sustained interest as the most pressing component of grit. Correlational analysis reveals that grit has a weak connection with course grades (short-term goal) but demonstrates a strong positive association with professional achievement (long-term goal) and maturation. Collectively, our findings substantiate that breakthrough accomplishment is contingent not solely on cognitive ability but on constant interests and resilience.

Pothole detection is critical in defining optimal road management solutions and maintenance. The researcher used deep learning and yolov3 to create a pothole detection system in this study. A deep learning algorithm called YOLOv3 is used to develop a model that can successfully identify potholes. The detection model had an average precision of 95.43%, and identified potholes had accuracies ranging from 33% to 69%, which is to be anticipated given the numerous various forms and sizes of potholes.

Immediate identification of plant disease is one of the important solutions in Agricultural problems. In this study, the researchers develop an early detection system for tomato leaf diseases. It is important to create a system that will detect and classify a certain disease present in the leaf to prevent further loss. In order to do that, the researchers used an algorithm called YOLOv3 for training a model that accurately detects specific diseases for tomato leaves. The proposed model is able to classify the diseases and has a mean average precision(mAP) of 98.28 %. The result of the trained model varied with the accuracies ranging from 75% - 99%, for detecting the two common tomato leaf diseases such as, Early Blight and Septoria Leaf Spot.

The emergence of information technology used in all factors of our everyday lives has exponentially increased the amount of unstructured data. This huge quantity of records is a great source for finding and thus, may be used for extracting actionable information. In the academe, for instance, teachers and school administrators can adjust their approach to teaching/learning by getting feedback from students through a Learning Management System that can automatically analyze the semantic orientation of words and contextual polarity of these feedbacks - categorizing them into positive and negative. Identifying and classifying words expressed in the students' feedback about learning materials can provide structured information that can guide the teacher, impact its design and target the students' needs. This study implemented a lexicon-based strategy for automatic sentiment analysis using VADER as a model. Student feedbacks are extracted from an LMS developed to demonstrate the usability and effectiveness of the adopted approach; among other features of LMS that will help teachers improve its implementation. Results of the LMS sentiment analysis are compared to human-annotated sentiments to verify and validate the output, as well as, check its accuracy using Confusion Matrix. It aims to create a structured representation of student sentiments through LMS to help teachers improve the design of learning materials.

Forecasting the number of incidences of medical cases is important in planning institutional health program strategies to draft intervention and allocate resources. The utilization of advancements in computing and the use of massive health data create possibilities for the generation of tools in a recommender system. This study focused on medical cases forecasting using exponential smoothing model for the development of resource allocation recommender system. Different data pre-processing techniques were used such as imputation and data cleaning in the historical dataset. To determine which set of alpha values can be considered and be used in the development of online resource allocation recommender system for Mabalacat City Health Unit, the mean absolute percent error and mean absolute deviation were used. Exponential smoothing with an alpha value of 0.9 and 0.3 have high forecasted values than that of Exponential smoothing using 0.1, 0.5 and 0.7 respectively.