Natural disasters caused large damage not only to infrastructure and human injuries, but also to a country’s economy. The disruption may affect a single economic sector but sector interdependence guarantees that this will trickle down to other sectors, leading to increased damages across overall economic sectors. This study investigates the impact of a disruption in a particular economic sector and its ripple effects to other interdependent sectors. An optimization model was developed by integrating inoperability input-output modeling (IIM) with grey multi-objective programming (GMP) to determine the optimal output level of any industry sector, which can cover the inoperability, that minimizes the cost of production and economic loss when disruption occurs. Uncertainty was incorporated in the model through the use of grey numbers. The efficacy of maintaining an inventory prior to disaster was verified through dynamic inoperability input-output modeling (DIIM) on how it delays inoperability, how operability in interdependent sectors is sustained and how economic losses are reduced. Two sectors, banana and coconut sectors, were used as case study in the application and verification of the model. The results showed that in terms of both inoperability and economic losses, the critical sectors are chemical and chemical products, and agricultural activities and services. The GMP model was able to determine the optimal total output level and initial inventory of a sector. Around 19% of banana sector’s original total output and 10%-11% of the coconut’s original total output should be produced as buffer to maintain operability given the specified levels of disruption in this study. A significant reduction in and delayed onset of inoperability values and economic losses is observed when on-hand inventory is maintained, and thus ensuring sector operability for some time following the disruption. The inoperability on banana sector was delayed by 2 months while 16-18 months for coconut sector. Sensitivity analysis was done on two parameters: (1) different weights used in the goal programming and (2) different levels of demand-perturbation in the IIM phase. Using various combination of weights still yield the same optimal solution for the lower and upper bound for both Banana and Coconut data. Employing different levels of demand-perturbation on the data generated the same critical sectors in terms of inoperability and economic loss. Changes on the level of disruption varied the optimal output and objective function. As the level of disruption increases, the economic loss and the production level also increase.

The Philippines is highly vulnerable to tropical cyclones (TCs), which frequently cause devastating impacts on infrastructure and communities. Rapid and accurate identification of damage extent and location is essential to trigger appropriate post-disaster response, expedite recovery, and facilitate better reconstruction. This study developed a methodology to classify building damage using Unmanned Aerial Vehicle (UAV) images collected in February 2014 after Typhoon Haiyan hit the study area located in Tacloban City in November 2013. The methodology employed Structure-from-Motion (SfM) technique, texture analysis, and topographic modeling to analyze and extract building damage information. Correlation-based feature selection algorithm was used to refine and reduce the possible building damage predictor attributes. Random Forest was used to predict each building’s level of damage. Binary model R-A1, which classified completely damaged and undamaged buildings, had an accuracy of 93.5%, average precision of 0.938, average recall of 0.935 and average f-measure of 0.935, while model R-A2, which classified damaged and undamaged buildings, had an accuracy of 80.3%, average precision of 0.803, average recall of 0.803 and average f-measure of 0.803. Ternary model R-B1, which classified completely damaged, partially damaged and undamaged buildings, had an accuracy of 81.6%, average precision of 0.812, average recall of 0.816, and average f-measure of 0.813. The R-A2 model had an accuracy of 73.4% when tasked to classify 613 previously unseen damaged buildings. The R-B1 model had an accuracy of 70.3% when tasked to classify 575 previously unseen partially damaged and completely damaged buildings. This study highlighted the challenges in identifying and classifying building damage markers, some of which are unique to the Philippine setting, and demonstrated the value of UAV-based assessments for rapid and high-resolution damage evaluation.

In order to fully realize the potential of water electrolysis in the transition to sustainable energy systems, catalysts that counter the slow kinetics of the H2 evolution reaction (HER) in the cathode and the O2 evolution reaction (OER) in the anode are needed. Among the emerging materials, transition metal phosphide (TMP) catalysts have promising activity and stability in HER and OER. In particular, nickel phosphides perform well as electrocatalysts due to their efficient electron transfer between the transition metal and phosphorus sites. Nickel phosphides can be prepared through various synthesis methods. One of them is the emerging technique, the dynamic H2 bubble template (DHBT) method, a facile and dynamic technique that can yield porous morphologies with numerous active sites.

This work investigated the applied potential and phosphorus source (NaH2PO2) concentration parameters for DHBT to fabricate porous, active, and stable OER and HER Ni-P catalysts. Among the samples, NiP-500, which was synthesized with an applied potential of -4 V vs. Ag/AgCl and a NaH2PO2 concentration of 500 mM, yielded the best electrochemical performance for alkaline water electrolysis. It only required a low overpotential at 10 mA cm- of 265 mV for OER and 154 mV for HER. NiP-500 also exhibited good kinetics, with low Tafel slopes and charge transfer resistances for both reactions. Moreover, the catalyst exhibited minimal change in overpotential even after 12 hours of high-current density operation. Specifically, NiP-500 only had 2.93% and 2.72% increases for OER and HER, respectively. These good catalytic properties were brought upon by the catalyst’s semi-crystalline structure, porous morphology, and tuned composition, which were observed through various materials characterization methods. Further evaluation of the Ni-P catalyst’s bifunctionality as water electrolyzer anode and cathode also yielded promising results, registering a low cell potential of 1.64 V at 10 mA cm-2 and 1.88 V at 100 mA cm-2.

Overall, this work synthesized porous and bifunctional Ni-P OER/HER catalysts and provided insights into the development of materials through the DHBT synthesis method.

Approximately 75,000 tons of rubber tire waste are disposed annually in the Philippines (Philippine Statistics Authority, 2019) while energy demand may reach 99.3 MTOE in 2040 (Department of Energy, 2020). Rubber tire waste (RTW) may serve as feedstock due to its waste generation rate, calorific value, and low moisture content. This study utilized pyrolysis for energy conversion technology since RTW consists of 60-65% hazardous Styrene-Butadiene Rubber (SBR). An unmodified spark ignition internal combustion engine generator system was used for the energy generation technology. The experimental set-up was optimized at 550°C with 30 minutes retention time and waste reduction rate (WRR) of 47.42%. The engine-generator's specific fuel consumption (SFC) and heating values for synthetic gas and synthetic oil were 4.77 kg/kWh, 0.35 kg/kWh, 3.18 MJ/m3, and 37.4 MJ/kg. The CO and CO2 emissions were 120.03 ppm and 4.25% v/v, respectively. Furthermore, each kilogram of RTW processed yields 0.169 kg of diesel equivalent synoil and 0.046 kg of gasoline equivalent syngas.
The study confirms that the Pyrolyzer-Spark-Ignition-Internal-Combustion-Engine-Generator-System (PSIICEGS) can recover energy from RTW for power generation and that the experimental set-up is operational with 100% syngas. It is advised that more research be done to improve the system performance and efficiency.

This study developed and validated a comprehensive User Experience (UX) evaluation tool specifically designed for mental health mobile applications. Through a systematic process, the research identified 12 key domains crucial for evaluation, including Usability, Usefulness, Desirability, and Therapeutic Quality, among others. The initial instrument, comprising 48 items, underwent validation through expert panel review for face and content validity, resulting in refinements that expanded it to 50 items. Pilot testing with 19 college students using a selected Mental Health app yielded a Cronbach's Alpha of 0.972, indicating excellent reliability. Factor analysis reduced the instrument down to 16 items and revealed four distinct components: Perceived Effectiveness and User Engagement, Interoperability, Intention to Use, and Usability and Design. These factors explained 72.046% of the total variance, with all items exceeding communalities of 0.60. The reliability analysis for each factor yielded the following Cronbach's Alpha values: Perceived Effectiveness and User Engagement (0.875), Interoperability (0.833), Intention to Use (0.907), and Usability and Design (0.855). The overall instrument's reliability analysis yielded a Cronbach's Alpha of 0.922, indicating excellent internal consistency.
The study also compared the new instrument with the MAUX-C instrument by evaluating the same pre-selected app, highlighting its strengths in providing a more comprehensive and user-centered evaluation of mental health apps.