The occurrence of microplastics in freshwater systems is of growing concern and being studied worldwide. Rivers serve as channels for carrying microplastics to other water bodies, such as lakes and oceans. In the Philippines, Laguna Lake is considered to be the largest lake and known for its many uses. One of its tributaries is the Taguig River. This study is the first to evaluate the abundance, characteristics, and distribution of microplastics along Taguig River to Laguna Lake in surface water, water column, and sediments during dry and wet seasons. Samples were collected, extracted, and examined under a 40x magnification microscope according to size, color, and shape. For the dry season, results showed that the concentrations of microplastics are 107-390 particles/m3 in surface water, 47-370 particles/m3 in water column, and 76-119 particles/kg in
sediments. For the wet season, concentrations are 60-103 particles/m3 in surface water, 30-77 particles/m3 in water column, and 43-129 particles/kg in sediments. The concentration of microplastics is higher during the dry season than the wet season. The average ratio of microplastic count in water column to surface water is 0.57. The dominant colors found are transparent, blue, and white. Microplastics in the form of fiber and films are abundant in water samples and sediments, respectively. Representative samples analyzed using a Fourier-Transform Infrared (FTIR) spectroscopy revealed that the polymer types present are polyethylene (PE), polypropylene (PP), polyethylene terephthalate (PET), and polystyrene (PS). Further, the total microplastic pollution load in water samples of Taguig River contributing to Laguna Lake is approximately 2-61 particles/s.

The capacity of an Asphalt Pavement to resist loads depends on its temperature due to its viscoelasticity. Because of this, measurements gathered from field tests vary due to different temperatures experienced by the pavement. Temperature correction procedures are developed by different countries to properly address this problem. However, in the Philippine setting, such temperature correction procedures are yet to be established. A reference temperature should also be set to standardize FWD measurements, however, no reference temperature is yet to be established for perusal in the Philippines. The study aimed to bridge this gap by conducting an experiment that collected temperatures at three (3) set depths; pavement surface, mid depth, and third depth on a test pavement located at the Ninoy Aquino International Airport (NAIA). Using these measurements, a temperature prediction model based on polynomial regression analysis and elastic net regularization technique was developed. The application of the elastic net regularization technique minimized highly correlated pavement temperature factors to simplify model inputs and be used with ease during FWD tests. The model performed better than existing models with an r-squared value of 0.932 and 0.864 at mid and third depths respectively with errors lower than existing models in comparison. It was recommended to utilize the temperature prediction model to develop standard procedures in using the Falling Weight Deflectometer (FWD) in monitoring the structural capacity of pavements and to establish a local reference temperature for temperature correction procedure.

The coconut tree, often called the "tree of life," is renowned for its diverse applications, serving as a vital source of food and as a renewable energy resource in the form of biodiesel. With the growing emphasis on sustainable and climate-sensitive development, the demand for coconuts is projected to rise, potentially leading to conflicts between its use for food and energy. This highlights the urgent need for effective resource management and systematic monitoring of national coconut resources. In the Philippines, however, the integration of modern technologies into natural resource management remains slow. To address this challenge, this study explores the enhancement of UAV remote sensing through the application of deep learning techniques, particularly for mapping coconut tree crowns. Two models, the Segment Anything Model (SAM)—a Vision Foundation Model trained on extensive web-scale data—and U-Net, a conventional convolutional neural network, were evaluated for their ability to perform automatic and unsupervised coconut tree crown delineation through a binary semantic segmentation process. Fine-tuning strategies for SAM, as well as multiscale and multimodal training methods were implemented for both models to assess their capabilities for this task. Results indicate that U-Net outperformed SAM, achieving higher segmentation accuracy with average F1-Scores and IoU values of 0.889 and 0.848, respectively, compared to SAM’s 0.772 and 0.73 using probability threshold value of 0.5. U-Net also demonstrated superior performance in multiscale training, with an average increase of 0.128 in F1-Score and 0.194 in IoU. However, multimodal training yielded suboptimal results for both models mainly due to the lack of model optimization for such data. For SAM, its image encoder’s bias towards RGB datasets can explain its low segmentation performance using the multimodal data. The lack of further preprocessing and normalization of the multimodal data could have also caused the low accuracy of both models. Additionally, U-Net excelled in training efficiency, confirming its better suitability for the binary segmentation tasks. Despite these differences, SAM exhibited robust discriminatory performance and was effectively fine-tuned with a 20x20 point grid prompt, enabling its application in automated and unsupervised segmentation tasks. This study successfully enhanced UAV remote sensing with deep learning
techniques, advancing resource management capabilities for mapping applications. The combined use of UAVs and deep learning provides a more efficient and evidence-based approach to resource mapping, as demonstrated in coconut tree crown delineation. While U-Net proved to be the more effective model for this specific task, SAM’s adaptability
suggests potential for broader segmentation applications.

Mechanical defects, like scratches in corrosion protective coating systems, are unavoidable in practical engineering applications. Coatings with self-healing properties represent a new development against such occurrences. This study developed and evaluated the use of Superabsorbent Polymers (SAP) as a self-healing additive to epoxy
paint systems.

The SAP underwent preliminary characterization to determine its primary properties and was then incorporated into the epoxy primer at different percentages. The epoxy-SAP composite was subjected to thermal analysis to observe the possible effects and interactions of their mixing and then the coating system underwent corrosion testing via electrochemical impedance spectroscopy, adhesion test, and long-term immersion test to evaluate their self-healing capability.

Through characterization tests, the SAP is determined to have an average size of 165 μm by measuring its Feret diameter and FTIR results confirms the polymer structure and functional groups present in the SAP. It is also determined that the SAP has a free swelling capacity of 25.23 g/g and a swelling rate of 0.435 g/gs at 3.5 wt.% NaCl solution. Thermal analysis using Thermogravimetric Analysis (TGA) and Differential Scanning Calorimetry (DSC) showed that the epoxy curing process does not result to the decomposition of SAP and this is supported by visual inspection of the side profile of the coating under an optical microscope; the curing instead results to the removal of moisturewithin the SAP structure. The adhesion of the epoxy-SAP coating system is tested using ASTM D3359-17 and excellent adhesion was obtained with the coating system composed
of initial epoxy primer, epoxy-SAP layer, final epoxy primer, and an epoxy enamel topcoat because no peeling or removal of the coating was observed.

Electrochemical Impedance Spectroscopy (EIS) was used to determine the corrosion behavior of the epoxy-SAP system. The addition of SAP increased the charge transfer resistance, increased the pore resistance, and decreased the double layer capacitance of the coating system in a span of 5 hours, confirming the self-healing of the coating. Maximum corrosion protection of the metal substrate for 5 hours is obtained at SAP concentrations between 15 to 20 wt.% SAP. Below 15 wt.% SAP, the self-healing is insufficient and above 20 wt.% SAP, the SAP would swell too much causing electrolyte penetration to the coating. Lastly, it is determined through long term immersion test that the addition of SAP in the coating significantly decreases the leaking of the rust from the scratch; however at all concentrations of SAP, the coating will start to peel off due to the swelling of SAP after 2-3 days.

Virgin Coconut Oil (VCO) is a valuable commodity with significant health benefits, making its efficient production a key concern in the coconut industry. The commonly used process to produce VCO is the natural fermentation (VCO-NF) process owing to its low investment cost requirement. Despite the extensive published research, this process also has the lowest yield (< 20%) among the VCO extraction technologies (e.g., expellerpressing and centrifugation). Since most yield improvement studies entail costly improvements, such as adding certain microbial cultures, enzymes, or utilizing advanced types of machinery, VCO-NF producers fail to adopt them. Using Production Yield Analysis (PYA) and Response Surface Methodology – Box-Behnken Design (BBD), this study aimed to optimize the VCO-NF process and increase the yield by identifying the optimal conditions for the fermentation factors that can be controlled under factory conditions, such as milk-to-water ratio, stirring time, and fermentation time. Results show that oil yield is maximized at 32% by using a 1:1 milk-to-water ratio, stirring for 15 minutes, and fermenting for 48 hours. The optimized parameters were validated in industry-scale production (1500 nuts used per batch) to determine their applicability. The average industry-scale oil yield (%) is 37%, making the optimized parameters 114% applicable to high-volume production. This study provides a systematic, repeatable, and readily implementable approach that can contribute to sustainable practices and economic growth in coconut-producing areas. The quality of the produced oil also adheres to corresponding standards, ensuring global market competitiveness and profitability