The increasing frequency and intensity of natural hazards highlight the limitations of traditional disaster monitoring systems that rely on static sensors and delayed reporting. Advances in the Internet of Things and computer vision now enable distributed, real-time observation across multiple hazards. IoT networks provide fine-scale measurements of environmental and structural parameters, while vision systems using cameras, drones, and satellite imagery deliver spatial verification and automated impact assessment through artificial intelligence. When integrated, these technologies improve detection accuracy, shorten response times, and strengthen situational awareness. This review synthesizes recent global developments across three dimensions: technology, resilience, and governance. The analysis examines hybrid architectures that merge IoT and vision systems, evaluates continuity strategies such as renewable-powered microgrids and UAV-based communications, and identifies governance challenges involving interoperability, privacy, and institutional coordination. A layered conceptual framework is proposed to link sensing, analytics, alerting, and policy mechanisms. Findings reveal persistent gaps in endurance during extended outages, the need for generalizable multihazard fusion models, and the importance of ethical data governance. The synthesis provides guidance for integrating IoT-vision systems into resilient, scalable, and inclusive early warning infrastructures aligned with global sustainability and disaster-risk-reduction goals.

Riverbank slope failures are a major concern in many regions of the Philippines, particularly in low-lying areas and near major rivers, where recurrent flooding and soil erosion can cause significant infrastructure damage. Accurate assessment of slope stability is crucial for mitigating these risks, and various methods are available to calculate the Factor of Safety (FOS) for slopes. This study compares the FOS values for riverbank slope stability using five common methods of slices: the Morgenstern-Price, Janbu, Bishop, Ordinary, and Spencer methods. There are three hundred seventy-five (375) simulations that were used in the slope stability analysis from the six (6) riverbank slopes and 15 different soil properties. A statistical analysis (ANOVA) revealed no significant differences in FOS values among the methods, indicating that all are reliable for assessing the stability of riverbank slopes. However, the methods demonstrated varying sensitivities to changes in soil properties and slope angles, underscoring the importance of considering their specific assumptions and limitations in practical applications. Further research is recommended to examine the effects of fluctuating water levels, seismic activity, and other environmental factors on slope stability, as well as to explore the integration of advanced numerical methods, such as finite element analysis, for deeper insights into slope failure mechanisms.

The widespread use of Liquefied Petroleum Gas (LPG) in households and industries presents a significant risk due to its highly flammable nature, making early detection of leaks for preventing accidents. Traditional LPG detection methods often rely on manual monitoring, which may not provide timely alerts or automatic responses to prevent accidents. The Internet of Things (IoT) refers to a network of physical objects, or “things,” embedded with sensors, software, and other technologies that enable them to connect and exchange data with other devices and systems over the internet. The advantage of the Internet of Things (IoT) offers new opportunities to safety systems through real-time monitoring and remote alerts. By integrating IoT technology with LPG leak detection, it is possible to create a responsive and reliable safety system. This device focuses on the development of an IoT-based LPG tank leakage detection system with alarm and auto-off system, designed to detect leaks, sound an alarm, and automatically shut off the gas supply or even remotely turn off the valve through an android-based application to prevent accidents. The system uses IoT for real-time data transmission and remote monitoring, allowing users to receive instant notifications and take immediate action, even when away from the premises. The system uses gas sensors to continuously monitor LPG levels, triggering an immediate response in the event of a leak. Integrated with IoT technology, the system provides real-time alerts to users via android-based application, ensuring prompt action.

Stroke, a leading cause of brain-related illnesses and mortality, occurs due to the blockage of blood flow to the brain or the rupture of blood vessels, leading to brain damage and long-term disability. A common consequence is weakness or paralysis, such as left-sided hemiplegia, which significantly impacts independence and daily activities. As technology advances, there is a growing need for innovative solutions to enhance the rehabilitation journey and empower stroke survivors to regain arm function. Traditional rehabilitation methods, often reliant on physical therapy and Neuromuscular Electrical Stimulation (NMES), may not fully optimize upper limb recovery, and accessibility to these treatments poses challenges, especially for patients with mobility issues. The ARMeD (Assistive Rehabilitation Monitoring Device) addresses these challenges by integrating NMES with Passive Range of Motion (PROM) during rehabilitation. This groundbreaking device, accessible via an Android app, allows patients to undergo rehabilitation from home while enabling physical therapists to monitor their recovery progress effectively.

Outcome-Based Education (OBE) emphasizes the importance of defining and assessing specific learning outcomes. Effective monitoring of these outcomes is crucial for ensuring student success and program effectiveness. Previous research has explored various approaches to enhance program outcome monitoring, however, have not fully addressed the need for individualized and comprehensive progress tracking that goes beyond binary pass or fail measurements. This paper presents a novel approach to enhance program outcome monitoring through the development of individualized dashboards and the application of an adaptive neuro-fuzzy logic (ANFIS) framework. Data were derived from CSV reports of students in a learning management system and Canvas New Analytics from a sample class in the pilot study. The ANFIS framework is based on formative and summative assessments, total and maximum page views and participation, and average weekly page views and participation. The ANFIS model and dashboard results demonstrate its effectiveness in providing students and educators with a deeper understanding of student progress in terms of program outcomes, enabling targeted interventions and personalized learning experiences. This comprehensive approach empowers educators with the tools and insights needed to optimize educational practices and ensure that all students achieve the desired learning outcomes.