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

N-acetyl-l -cysteine (NAC) is a widely used mucolytic and antioxidant agent with diverse therapeutic applications. However, due to its poor stability, NAC faces significant challenges in its tablet manufacturing. Spherical crystallization has emerged as a promising technique, transforming a crystalline pharmaceutical to a form suitable for direct tableting. This study aims to apply spherical crystallization to form spherical NAC with improved solid-state properties. A systematic solvent system screening was conducted wherein spherical NAC was successfully obtained via a quasi-emulsion solvent diffusion (QESD) technique with water as the solvent and ethyl acetate as the antisolvent. The spherical crystals presented a hollow structure correlating to the QESD technique, with sphericity and roundness of 0.98 and 0.88, respectively. Spherical NAC bulk properties showed significant improvements in flowability and compressibility. Lastly, exploratory investigation on QESD showed that the addition of Tween 80 as a surfactant significantly affected particle morphology. Overall, the findings suggest that spherical crystallization is a useful technique for enhancing the flowability of NAC

Optimization of interconnect flow channel design is critical for maximizing SOFC stack performance and efficiency as it significantly affects gas distribution, current collection, and heat management. This study evaluated the impact of alternating narrowing- broadening (ANB) channels on SOFC performance, focusing on current and power generation, fuel utilization, and the distribution of pressure, velocity, current density, and gas species. ANB channel design targets to create a pressure gradient perpendicular to the channels, promoting better diffusion of gas species into the electrode regions covered by the interconnect ribs. This study investigated the effects of (i) channel geometry (parallel straight (PS) vs. ANB), (ii) broad width-narrow width ratio (BNR), and (iii) channel height via ANSYS software. Results revealed that ANB channels significantly improved fuel utilization to 42.45% and generated a peak power density (PPD) of 1.03 W/cm², a 13.8% increase compared to conventional PS. The significant enhancement in performance was attributed to the optimized gas distribution facilitated by the ANB channel design. This improved gas distribution minimizes mass transport limitations, leading to reduced concentration polarization and accelerated electrochemical reaction kinetics, ultimately resulting in higher power density. Moreover, decreasing BNR from 3.0 to 1.5 further enhanced PPD by 9.71% due to increased electrode area directly exposed to gas reactants, slower gas velocities, and improved reactant consumption. Reducing channel height to 0.5 mm initially caused O2 starvation, which could be an indication that oxidant consumption was enhanced, but was resolved by increasing the O2 mole fraction in the cathode inlet. This reduction in channel height improved PPD by 4.71% through enhanced gas distribution due to the increase in pressure when the area perpendicular to flow was reduced. Findings of this study could be pivotal for future design considerations as more research on SOFC performance improvement is needed to accelerate its commercialization.