Mount Hamiguitan Range Wildlife Sanctuary is the only mountain range heritage site among the six UNESCO World Heritage Sites found in the Philippines. It is the largest pygmy `bonsai' forest rich with biodiversity of endangered, endemic and rare species of flora that attract not only tourists but also foreign researchers. However, both tourism and research could threaten the sanctuary's conservation if not managed well. Through this study, the Mount Hamiguitan's different flora species, their habitat, and its distribution can be located on the map while doing exploration using a location-based mobile application named as HAGO (Hamiguitan-Go). A mountain explorer who will use the HAGO APP will act as a PS (Participatory Sensing) using his/her smartphone's built-in GPS, internet and camera to geotag the captured plant he/she encountered while hiking. Once verified, the geotagged picture and its information details are stored in an online repository system of plants. The APP users can look into the lists of plants they want to explore and locate on the map using geo-location mapping. Further developments and several on-site experiments must be conducted to achieve a fully-functional cloud-based system.

Mid-sized Philippine cities commonly rely on fixed-time traffic signal plans that cannot respond to short-term, demand-driven surges, resulting in measurable idle time at stop lines, increased delay, and unnecessary emissions, while adaptive signal control has demonstrated performance benefits, many existing solutions depend on centralized infrastructure and high-bandwidth connectivity, limiting their applicability for resource-constrained local government units (LGUs). This study reports a field deployment of TrafficEZ, a lightweight edge AI signal controller that reallocates green splits locally using traffic-density approximations derived from cabinet-mounted cameras. The controller follows a macroscopic, cycle-level control abstraction consistent with Transportation System Models (TSMs) and does not rely on stationary flow–density–speed (fundamental diagram) assumptions. The system estimates queued demand and discharge efficiency on-device and updates green time each cycle without altering cycle length, intergreen intervals, or pedestrian safety timings. A quasi-experimental pre–post evaluation was conducted at three signalized intersections in El Salvador City using an existing 125 s, three-phase fixed-time plan as the baseline. Observed field results show average per-vehicle delay reductions of 18–32%, with reclaimed effective green translating into approximately 50–200 additional vehicles per hour served at the busiest approaches. Box-occupancy durations shortened, indicating reduced spillback risk, while conservative idle-time estimates imply corresponding CO₂ savings during peak periods. Because all decisions run locally within the signal cabinet, operation remained robust during backhaul interruptions and supported incremental, intersection-by-intersection deployment; per-cycle actions were logged to support auditability and governance reporting. These findings demonstrate that density-driven edge AI can deliver practical mobility, reliability, and sustainability gains for LGUs while supporting evidence-based governance and performance reporting.

Engineering programmes increasingly aim to develop graduates who can integrate disciplinary knowledge with transferable competencies such as communication, teamwork, ethical awareness and lifelong learning. To understand whether these outcomes are being achieved (and to improve teaching and curricula), educators need more than individual student assessments: they need systematic evaluation of outcomes at module, programme, and institutional levels. This chapter distinguishes assessment (methods that generate evidence of student performance) from evaluation (the process of interpreting aggregated evidence, together with stakeholder perspectives, to judge quality and inform decisions). Building on outcome-based education and quality assurance practices, we present a practical approach to designing an outcomes evaluation plan. The approach starts by clarifying purposes and evaluation questions, translating outcomes into indicators and performance criteria, and mapping where evidence is generated across the curriculum. It then combines direct and indirect measures to triangulate findings and strengthen interpretations. Finally, the chapter emphasises closing the loop: using results to guide curriculum development, educational policies, and staff development, and documenting continuous improvement for internal and external stakeholders.

This study advances the field of solar irradiance nowcasting by introducing a discrete-level classification approach, diverging from traditional continuous measurement methods. Grounded in a need for cost-effective and modular solutions, the research employs high-resolution computer vision coupled with a deep learning framework to predict Direct Normal Irradiance (DNI). By harnessing the capabilities of a Logitech C900 1080p HD camera and an NVIDIA Jetson module, the study achieves real-time data processing, pivotal for CSP systems' operational efficiency. The core of the methodology is the ResNet-50 convolutional neural network, refined via transfer learning on a bespoke dataset, culminating in a predictive accuracy of 85.78%. This discrete classification model contrasts with conventional, costly instruments like the MS-57, offering a novel and accessible alternative for DNI estimation. Such innovation not only demonstrates high predictive accuracy but also signifies a shift towards less resource-intensive and more adaptable solar energy forecasting tools, contributing a significant leap towards optimizing renewable energy systems.