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.

Flooding is the most frequent natural disaster globally, causing over $4 trillion economic losses over the past 40 years. By 2050, 1.47 billion people are expected to be exposed to flooding, particularly in high-risk coastal areas in Asia. Building resilient coastal communities requires the design of sufficiently robust and cost-effective flood infrastructures. The design criteria for flood infrastructure usually involves flood frequency analysis (FFA) of historical data of flood drivers, which calculates return period (RP) and failure probabilities (FP). Traditional FFA assumes that flood drivers are under the notion of independence. Recent studies showed that flood drivers are interdependent and interact with each other, leading to compound flooding, which has more severe consequences than individual flooding events. Most of the existing multivariate techniques for flood assessment fail to capture complex, non-linear dependencies, often underestimating risks. Copulas provide a more flexible approach by modeling joint dependencies separately from marginal distributions, allowing for a more accurate representation of the joint behavior of flood drivers, crucial for improving flood risk assessments and infrastructure design.

This study employed copula modeling to explore the interactions and interdependencies of the flood drivers at 69 locations across the Philippine coast. Flood driver data were fitted to 11 parametric marginal probability distributions, with Maximum Likelihood Estimation (MLE) used for parameter estimation, the Akaike Information Criterion (AIC) for model selection, and the Kolmogorov-Smirnov (KS) test for goodness-of-fit. Lag dependencies were also analyzed to understand the temporal interactions between variables. Pairs of marginals at each location were modeled using 15 copula functions from both symmetric and Archimedean families, with MLE for parameter estimation, AIC for model selection, and the KS test for goodness-of-fit. Monte Carlo simulations were performed to quantify uncertainty and generate 95% credible intervals for key quantiles (0.99, 0.98, 0.96, 0.9, and 0.5). Joint return periods (100-, 50-, 25-, 10-, and 2-year) were calculated for different bivariate scenarios (OR, AND, and Kendall) and compared to the univariate scenario, revealing that 81% of coastal communities face underestimation of risk. FPs across all RPs and scenarios consistently showed higher failure rates for the co-occurrence scenario compared to the univariate scenario, suggesting that flood infrastructure designed under the assumption of independence may not withstand projected flood magnitudes. Finally, this method can be extended to various flood variables, enabling a comprehensive understanding of flood profiles across different regions of the country, including different combinations of flood drivers, characteristics, and magnitudes, and informing the development of more robust flood infrastructure design criteria.

Core-periphery structure is a mesoscale structure that has been observed in realworld networks, composed of a densely connected set of core nodes, with a sparsely connected set of periphery nodes. Variants such as multiple core-periphery pairs or multicore-periphery have been proposed to capture different structures in real-world networks, and several methods have also been proposed to detect these different structures. Cucuringu et al. proposed a single core-periphery detection method
LOWRANK-CORE that uses a low-rank matrix approximation of the adjacency matrix of a graph. This study aims to develop an algorithm capable of detecting coreperiphery structure in graphs, motivated by the LOWRANK-CORE algorithm, but is based on a different observation, specifically the characteristic polynomial of the adjacency matrix of ideal single core-periphery and multiple core-periphery models.

This study investigates the antiviral potential of phytochemicals from Philippine medicinal plants against key dengue virus proteins—NS2b–NS3 protease, NS5 methyltransferase (Mtase), and the envelope (E) protein—through virtual screening, ADMET profiling, molecular docking, and molecular dynamics simulations. Out of 2,944 screened compounds, 1,265 were identified as pharmacologically viable. Several phytochemicals demonstrated strong binding affinities surpassing known reference ligands, suggesting their potential as dengue virus inhibitors. Notably, many of these compounds are derived from plants traditionally used in herbal medicine, highlighting the value of integrating ethnobotanical knowledge with computational drug discovery methods.

The Philippines, with its numerous off-grid islands, face challenges in reliable and affordable electricity supply. Currently, at least 132 small islands and isolated grids (SIIGs) depend on oil-based generators, resulting in high cost, unreliable, and inadequate power, which negatively impacts living standards, education, public health, and economic opportunities. This thesis developed a methodology for determining the least-cost voltage and timing of interconnecting island grids to the main grid. It compares the cost effectiveness of connecting to the main grid with that of continuing with oil-based generators with RE-based hybrid system. The interconnection scenario assesses the route and voltage level options for the least-cost interconnection scheme which is compared with the least-cost RE-based hybridization capacity expansion plan of continuing isolated island grid. The goal is to identify the most cost-effective solution including timing for improving power supply reliability and affordability for island grid interconnection in the Philippines. The methodology is applied to a case study of an interconnection of an off-grid island.