Volume 3 Series 2023

EcoCharge: Innovative Solar and Wind Charging Station Enabled by Solid Waste Contributions
Jon Michael Magat, Jan Piolo Guiao, Noel Malana, Mark Francis Pera, Reel Jerin Ignacio, Norman Bob Gomez

Abstract

This study presents an innovative approach to waste disposal by leveraging microcontroller-controlled charging stations powered by solar and wind energy. Utilizing advanced sensors, the system detects incoming waste inputs, while intuitive indicators display the available charging time for each chamber. By utilizing designated buttons, users can allocate the available time from the minute counter to the charge timer, enabling charging in the corresponding relay slots 1 to 3. Extensive analysis revealed the charging percentages of batteries, identifying the peak performance occurring between 11 A.M. to 1 P.M., regardless of load presence. Additionally, the study investigated the hourly battery percentage discharged in the absence of any power source.

Flooding, a destructive force of nature, has experienced a significant increase in both frequency and severity over recent decades. This calamity occurs when heavy rainfall causes rivers to overflow, resulting in numerous casualties and substantial economic losses. To address these alarming events, researchers have developed a river flood mapping system for the Cagayan River. By utilizing water level data obtained from the Buntun Bridge sensor, this system employs remote sensing and GIS techniques, particularly a Digital Elevation Model (DEM), to identify areas prone to flooding and visualize the extent of the flood. The system also identifies structures at risk and provides a list of affected families. To evaluate its usability, the study employed metrics such as efficiency, effectiveness, and satisfaction. The results revealed a high task completion rate and low user error, indicating successful task execution and demonstrating the system's well-designed and easily understandable nature.

The accuracy of cost estimates in the conceptualization stage of road construction projects can be affected by unforeseen factors and incomplete data, leading to delays in feasibility studies. To address this issue and optimize the utilization of government funds in Region II, this study proposes an efficient and effective early cost estimation method. An artificial neural network approach is employed to model the local cost of road projects. A dataset of 85 road projects is analyzed, collecting data on various factors including road type, location, road length, project duration, capacity, pavement thickness, pavement width, and shoulder width for each project. MATLAB software is utilized to conduct multiple simulations and determine the optimal model for total road project cost estimation. The model is trained using the Levenberg-Marquardt algorithm, enabling efficient parameter optimization to minimize road construction costs. This approach facilitates more accurate cost estimations, aiding decision-making throughout road construction projects. The most effective neural network architecture incorporates eight input variables, a hidden layer with 13 neurons, and one output variable. The proposed model demonstrates high accuracy in predicting the total cost of road projects, as evidenced by correlation coefficients of 0.99522, 0.9652, and 0.99635 during the training, validation, and testing phases, respectively. Implementing this neural network model enhances the accuracy of cost estimates, thereby facilitating informed decision-making and optimizing resource allocation in road construction projects.

The road infrastructure has experienced relatively gradual changes in comparison to the rapid advancements in vehicle safety technology. Retroreflective devices, such as Bott's dots, have long been utilized for lane separation and edge detection. Despite their diverse shapes and sizes, these devices heavily rely on external light sources for reflection. However, the emergence of Glow-in-the-Dark (GiD) material introduces an innovative solution capable of storing and emitting energy in visible light, presenting a promising alternative to retro-reflectivity. This research focuses on the development and testing of GiD concrete-based markers designed specifically for lane separation and edge detection. These markers not only enhance driver alertness but also provide visible light without necessitating external light sources. The study not only demonstrates the durability performance of the presented prototype but also includes a comprehensive cost comparison with traditional Bott's dots. Moreover, the potential applications of GiD-based raised pavement markers extend beyond road infrastructure to encompass architectural and aesthetic designs in diverse settings, such as buildings, parks, walkways, and bicycle lanes.

In the Philippines, a significant portion of agricultural land, spanning 2.7 million hectares, lacks irrigation facilities, relying on deep well pumps and rainfall. To address this issue, the Department of Agriculture of the Philippines has advocated for the widespread adoption of solar-powered irrigation systems (SPIS), emphasizing the need for technical and financial feasibility. This study aimed to identify and map potential locations for SPIS in the barangay of San Esteban, Alcala, and Cagayan, utilizing Remote Sensing and GIS techniques. The resulting suitability map was generated using the Analytical Hierarchy Process (AHP), in combination with GIS and Remote Sensing, considering various criteria for selecting suitable SPIS areas. Analysis of the data revealed that out of the total 743 hectares of land area in Barangay San Esteban, 286 hectares (38.42%) were found to be unsuitable, 404 hectares (54.37%) exhibited moderate suitability, and 53 hectares (7.13%) were deemed highly suitable for implementing SPIS.

Tuguegarao City's emergency response institutions rely on hotlines or cell phone numbers as their primary means of contact during emergencies. However, this method falls short, particularly when victims are far from immediate assistance or unattended. To address this issue, the developers have designed and implemented the RescueLink in Tuguegarao City, providing comprehensive emergency response services for incidents such as accidents, fires, floods, earthquakes, and crimes. The system efficiently manages the storage, retrieval, and archival of user and emergency report information for both managing institutions and users. Leveraging the Evolutionary Prototyping Model, the system utilizes the Laravel framework for front-end and back-end development, along with MySQL for robust database management. Key features of the system include emergency reporting, user and agency notifications, GPS-based victim tracking, and the generation of detailed emergency reports. The system's usability was evaluated through the USE questionnaire, with participants rating it across four dimensions. The results showed that 73% strongly agreed and 22% agreed with the system's usefulness. Furthermore, 71% and 76% of respondents strongly agreed with the ease of use and learning aspects, respectively. In terms of satisfaction, 78% of participants strongly agreed, and 19% agreed with the system's performance. These positive findings indicate that the system is perceived as highly beneficial to Tuguegarao City and its residents.

Multi-label image classification is capable of providing multiple diagnoses for a single retinal fundus image. In this research, we used the Classification Transformer, a general framework that exploits transformers to learn the complex dependencies between visual features and category labels. A new multi-label retinal fundus image dataset, the Ocular Disease Intelligent Recognition ODIR-5K, was used. The transformer-based model for fundus multi-label disease classification was optimized through extensive experimentation for image analysis and disease classification. In this work, we also addressed the class imbalance of the dataset using the weighted loss function PolyLoss and the oversampling method Local Perturbation Random Over-Sampling algorithm which has a model score of 81.3% on 10% resampling. It is shown that the approach outperforms previous methods with an Area Under the Curve score of 90.2%.

Clarity, an AI-powered Visual Studio Code extension, presents an innovative approach to enhancing code quality and fostering maintainable software development. Leveraging the capabilities of large language models (LLMs), Clarity provides context-aware suggestions for code improvements, including variable naming, function naming, and code comments. The results revealed a significant improvement in the quality of the code produced by the participants, as measured by readability, maintainability, and reduced bug count. Clarity stands out as a valuable tool for software developers seeking to enhance the clarity and maintainability of their code. Its AI-powered capabilities empower developers to write more efficient, bug-free, and readable code, ultimately leading to improved software quality and reduced development time.

The purpose of this study is to develop and evaluate a web-based barangay Issuance System for barangays called the “Barangay Document and Issuance System (BDIS).” Researchers used a development research approach to design and develop the web-based Barangay Document and Issuance System (BDIS). The Barangay Document and Issuance System (BDIS) was created using the Rapid Application Development (RAD) approach of the Systems Development Life Cycle (SDLC) and evaluated by IT professionals, IT practitioners, and barangay stakeholders. The acceptance of the created system was evaluated using the System Usability System for system evaluation. The average score for all participants who evaluated the system was 4.58, which translates to "strongly agree.” The Barangay Document and Issuance System (BDIS) was rated acceptable because respondents liked it and found it convenient and easy to use. Barangays will benefit from this system to reduce administrative work and process document requests. It also functions as a database of barangay records by implementing and using the Barangay Document and Issuance System (BDIS).

This study developed an aircon-to-fan plug that has a three-way control using a smartphone to help reduce power consumption when using window-type aircon. It utilized manual switching, timer switching, and temperature-based switching. The general operation of the device is managed by a microcontroller. A mobile application was developed using MIT App Inventor. This mobile application must first be connected to the device using Bluetooth technology for the different control mechanisms to work. The accuracy testing of the device in terms of manual switching, timer switching, and temperature switching shows that it worked as it was intended to. The device was able to reduce power consumption by 30% and energy costs by 37% over a 12-hour period of use. People who already have air conditioning in their homes will benefit from the device since it will allow them to lower the amount of energy they use while still maintaining a comfortable temperature inside their homes.