Western Mindanao State University

The Philippine mining sector exemplifies a persistent paradox: significant mineral wealth coexists with profound human insecurity in host communities. This paper moves beyond resource curse and governance gap narratives to introduce and theorize the Extractive Insecurity Paradox—the systematic process whereby the pursuit of state and corporate security through mineral extraction actively dismantles human security at the local level. Employing the 1994 UNDP human security framework as a diagnostic tool, we conduct a multi-dimensional analysis of the Philippine mining regime, revealing how vulnerabilities cascade across economic, food, health, environmental, personal, community, and political domains. Our analysis demonstrates that these insecurities are not policy failures but the logical outputs of a structure defined by a liberalized policy architecture, an enclave economy, and coercive governance. The paper argues that overcoming this paradox requires a foundational paradigm shift. We thus propose a human security-centered governance framework, built on three pillars: a mandatory Human Security Impact Assessment (HSIA) as a binding license to operate; Post-Extractive Community Trust Funds to transform rents into lasting resilience and sustainable development; and the substantive empowerment of Free, Prior, and Informed Consent (FPIC). By synthesizing diagnostic analysis with a prescriptive model, this paper offers a critical conceptual advancement for understanding and reforming extractive industries in the Philippines and other resource frontiers globally.

To meet rising energy demands and reduce fossil fuel dependence, the Philippines aims to integrate nuclear energy in the energy mix by 2032, including the possible revival of the inactive Bataan Nuclear Power Plant (BNPP), a 621-MWe Westinghouse Pressurized Water Reactor. This work presents an updated radiological and environmental impact assessment (REIA) of estimated atmospheric radionuclide releases during normal operation using recent local meteorological data. The Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model was used to simulate long-range transport and deposition for the 2023 wet and dry seasons. Meteorological data from Cubi Point Weather Station in Zambales confirmed dominant northeast winds from November to April and southwest winds from May to October, consistent with GDAS1 data. Annual effective dose from cloudshine, groundshine, and inhalation pathways was estimated at 1.93 x 10-5 mSv/yr for a critical residence 1.5 km to the northeast direction from the plant. This value is far below the ICRP 103 recommended public dose limit of 1 mSv/yr and lower than the previous Gaussian plume estimates. Major contributors were 3H (HTO) and 133Xe, accounting for approximately 50% and 30% of the total dose, respectively. The study highlights the applicability of HYSPLIT for REIA studies at prospective nuclear power plant sites.

A mobile application for grading mushrooms grown at home with the help of deep learning technology was created in the present study. It can be regarded as the solution to the problem of the lack of standardized systems of grading mushrooms. Such systems were not accessible to small-scale mushroom growers because of the complexity of the existing systems. There were used the following three deep learning models to solve the mentioned problem: YOLOv8, MobileNetV2, and EfficientNet-B1. The mobile application uses the technique of object detection and classification in order to classify the mushrooms taking into account their cap size, stem length, color, and general appearance. The model was built for common edible mushroom types that are grown by small-scale farmers in Zamboanga. The best result in testing was demonstrated by YOLOv8 with the test accuracy of $92.94 \%$, precision, recall, and F1-score of 95%, which is the smallest generalization gap of 1.46%. EfficientNet-B1 achieved the test accuracy of $92.77 \%$ with $94 \%$ precision and recall. The test accuracy of MobileNetV2 equals 90.92%, with the precision of 93%. The models were optimized using TensorFlow Lite quantization in order to reduce the file sizes of YOLOv8 model from 48.2 MB to 12.8 MB ($73.4 \%$).

BaTiO3 is a model perovskite oxide that holds promise for high efficiency photovoltaic devices and next-generation 3D printing because it can have tunable optoelectronic and mechanical properties. In this work, we utilized density functional theory calculations with the CASTEP code and explored BaTiO3 under hydrostatic pressure. Our simulations illustrate that pressure is accompanied by a strong tendency for significant structural contractions that are characterized by lattice parameter shrinkage, and bond lengths reductions. The electronic band gap is highly dependent upon pressure; 1.71 eV (indirect, M–G) at 0 GPa, followed by 1.94 eV at 100 GPa, then reduced to 1.64 eV at 200 GPa, and consistently lowered to 1.34 eV at 300 GPa and then narrowed to around 0.91 eV at 400–500GPa. These band gap alterations pushed the optical absorption edge to wavelengths which are more favorable for photovoltaic applications. Device simulations using SCAPS-1D of photovoltaic devices under pressure, also demonstrated improved performance under pressure. At 0 GPa, the device has a power conversion efficiency (PCE) of 15.23% with an open-circuit voltage (Voc) of 0.7664 V, a short-circuit current density (Jsc) of 24.73 mA/cm², and a fill factor (FF) of 80.34%. As pressure is applied, these parameters significantly increased: at 300 GPa, Voc improved to 0.7729 V, Jsc increased to 30.40 mA/cm², FF is 79.39%, PCE improved to 18.65%; at 400 GPa, Voc improved to 0.7840 V, Jsc was 45.61 mA/cm², FF was 84.53%, and PCE improved to 30.23%, and similar values were observed at 500 GPa. With the elastic constants determined, it can be seen that the measured stiffness increased, and the favorable trend toward higher ductility increased as pressure was applied and is in support of incorporating BaTiO3 into mechanically resilient 3D-printed parts. All of this points to pressure engineering as a viable method for optimizing BaTiO3 for multifunctional optoelectronic and advanced manufacturing applications.

The implementation of Internet of Things (IoT) technologies holds transformative potential for healthcare delivery in rural and underserved regions. This systematic review focuses on technological, organizational, and ethical barriers. A comprehensive literature search across six major academic databases yielded eleven empirical studies published between 2015 and 2025. The review adhered to PRISMA protocols, with data synthesized using the Synthesis Without Meta-Analysis (SWiM) guidelines. Findings revealed that technological challenges—such as unreliable internet connectivity, lack of device interoperability, and power supply issues—are widespread across rural implementations. Organizational limitations included inadequate training, resistance to workflow changes, and financial constraints. Ethical concerns primarily involved informed consent, data security, and privacy, often exacerbated by weak regulatory structures and digital illiteracy. Despite these obstacles, successful implementation efforts were marked by community engagement, phased integration strategies, and culturally adapted frameworks. The review underscores the necessity of comprehensive approaches that combine infrastructure development, workforce preparedness, and locally contextualized ethical safeguards. These findings provide a critical foundation for policymakers, system designers, and healthcare practitioners aiming to scale digital health solutions in low-resource settings.