The University of Mindanao is the largest private, non-sectarian university in the southern Philippine island of Mindanao. It is located in Davao City. Established in 1946, the University of Mindanao has ten branches spread over thirteen campuses in Southern Mindanao. It currently has an Autonomous Status per CEB Resolution No. 076-2009 with Category A (t) per CMO No. s. 2009 standing accredited by Commission on Higher Education (CHED). Its Accountancy, Computer Engineering and Information Technology programs are Centers of Development(COD) and its Business Administration, Criminology and Teacher Education programs are recognized as Centers of Excellence(COE) by CHED. It is hailed as the institution with the second highest number of programs accredited by PACUCOA in the country today.
This study utilizes descriptive and exploratory research design to determine the factor structure of science learning anxiety in the new normal. Qualitative data collection was carried out first by interviewing twenty (20) participants from different strands of the Senior High School department of UM Digos College. The identified items were transformed into a questionnaire for the quantitative phase and implemented to the two-hundred thirty-five (235) Senior High School students. Statistically, four factors of Science Learning Anxiety in the new normal were explored, these are; Attitude, Learner’s Interaction Towards Assessment and Activities, Learning Environment, and Disappointment. From this result, conducting a confirmatory analysis is recommended to confirm the dimensionality of science learning anxiety in the new normal and to review the explored factors in different contexts.
Concrete is commonly used in construction because of its high compressive strength and durability. However, its brittle nature makes it vulnerable to microcracks that can decrease long-term performance. This study evaluates coconut coir fiber (CCF) as a sustainable additive for self-healing concrete. CCF was incorporated as a partial cement replacement (1%, 3%, and 5% by weight), in both its natural form and as a carrier for a silica-based self-healing material (SHM). Physical and mechanical properties, including density, water absorption, compressive strength, and flexural strength, were examined together with crack-healing efficiency. The results showed that CCF exhibited high tensile strength (similar to 115.5 MPa). Specimens with SHM-CCF at 3% and 5% showed significant reductions in crack width after 28 days (about 75-80%), indicating enhanced self-healing ability. Although water absorption slightly increased with higher fiber content, concrete densities stayed within the normal range (2230-2290 kg m(-3)). The highest flexural strength (similar to 5.15 MPa) was reached at 1% SHM-CCF, while higher amounts caused reduced strength due to fiber agglomeration. Overall, SHM-CCF shows potential as a sustainable cementitious additive that improves crack healing and mechanical performance while utilizing agricultural waste.
Early identification of chronic kidney disease (CKD) remains challenging in resource-limited settings. This pilot study compares classical classifiers (SVM, XGBoost, kNN) and a stacking ensemble with a hybrid geometry-aware neural model that augments clinical features with topological summaries derived from persistence images. Using the UCI CKD dataset and a stratified $\mathbf{8 0} / \mathbf{2 0}$ train-test split, the held-out test performance in the reported run reached 0.988 accuracy for SVM, 1.000 for XGBoost, 0.975 for k-NN, and 1.000 for stacking. The proposed TDA-MLP also achieved $\mathbf{1. 0 0 0}$ accuracy and $F 1=1.000$ when evaluated using a decision threshold calibrated on training data only (stable tie-break) with $\mathrm{t}=0.50$; test AUC was 1.000 for all models in this run. Permutation importance indicated that both clinical variables and persistence-image features contributed to prediction, with several persistence-image coordinates ranking among the strongest predictors. Because results are based on a single split and a small held-out test set, the findings should be interpreted as exploratory and as an optimistic upper-bound estimate. Overall, the framework demonstrates that topology-based representations can be integrated into CKD screening pipelines while supporting more transparent model inspection.
Background: Tuberculosis (TB) remains a critical public health issue in the Philippines, a high-burden country. Understanding long-term trends is essential for evaluating the effectiveness of public health interventions. This study aims to analyse long-term trends and seasonal patterns of tuberculosis cases in the Philippines from 2016 to 2023. Using time series methods, it seeks to uncover significant trends, identify critical periods for intervention, generate short‑term forecasts, and provide insights to inform and enhance public health strategies. Methodology: This retrospective time series analysis used monthly TB notification data obtained from the Department of Health’s eFOI (electronic Freedom of Information) portal from 2016 to 2023. We used the STL (Seasonal and Trend decomposition using Loess) technique to isolate the long-term and seasonal patterns in TB notifications. Trend assessment was performed with the Mann-Kendall test. We also fitted a seasonal ARIMA(0,1,2)(0,1,1) model to the Box-Cox‑transformed series for forecasting. Results: The analysis revealed a significant upward trend in total TB cases, with a mean monthly increase of 0.80%. Seasonal peaks occurred in March, and troughs in December. The Mann-Kendall test confirmed the statistical significance of these trends ( <0.0001). New and relapse TB cases exhibited consistent increases, while retreatment cases showed a slight decline. The seasonal ARIMA forecasts project peaks of approximately 55,213 cases in March 2024 and 58,964 cases in March 2025, followed by mid‑year plateaus and December troughs. Conclusions: The study identified a persistent increase in TB cases, emphasizing the need for continued and enhanced public health efforts. Forecasted surges in March 2024 and March 2025 highlight the need to intensify active case finding in January-March and to allocate resources adaptively for 2024-2025. Seasonal patterns highlight critical periods for intervention, particularly in the first quarter of the year. These findings can guide more timely resource allocation and targeted TB control measures at both national and local levels.
Accurate forecasting of coal consumption and production is vital for strategic energy planning in the Philippines, where coal remains a primary energy source with a pronounced long-term upward trend. We propose a Topological Data-Augmented Grey Model (TDA-GM(1,1)), a first-order differential grey model that extends the classical GM(1,1) by integrating L1-norms of persistence landscapes from persistent homology as a topological driving term, to improve forecasting precision. By incorporating these topological features, TDA-GM(1,1) captures the complex, nonlinear dynamics inherent in coal consumption and production data influenced by economic and policy factors. Using historical annual data from 1977–2016 for training and 2017–2020 for testing, we evaluate performance via MSE, RMSE, MAE, and both in-sample and out-of-sample MAPE and compare TDA-GM(1,1) with benchmark models GM(1,1), DGM(1,1), ARIMA, Exponential Smoothing, and Linear Regression. We find that TDA-GM(1,1) achieves the lowest out-of-sample MAPE for both coal consumption and production while remaining competitive in ex-ante variance diagnostics. This model adapts to nonlinear trends, making it valuable for policymakers managing energy resources and planning. This study highlights the potential of TDA-enhanced grey models for small-sample energy time series and the broader applicability of TDA in forecasting complex dynamics.
