The research aims to develop a teacher's performance evaluation tool using opinion mining with sentiment analysis. The study may help to identify the strengths and weaknesses of the faculty members based on the positive and negative feedback of the students either in English or in Filipino language. The proposed system provides the sentiment score from the qualitative data and numerical response rating from the quantitative data of teachers evaluation. It will also graphically represent the evaluation result including the percentage of positive and negative feedback of the students. Thus, the school administrators and educators will be more aware about the sentiments and concerns of the students.

The generalized gradient approximation (GGA) to density functional theory (DFT) calculations indicate that the highly localized states derived from the defects of nitrogen doped carbon nanotube with divacancy (4ND-CNxNT) contribute to strong Sc and Ti bindings, which prevent metal aggregation. Comparison of the H2 adsorption capability of Sc over Ti-decorated 4ND-CNxNT shows that Ti cannot be used for reversible H2 storage due to its inherent high adsorption energy. The Sc/4ND-CNxNT possesses favorable adsorption and consecutive adsorption energy at the local-density approximation (LDA) and GGA level. Molecular dynamics (MD) study confirmed that the interaction between molecular hydrogen and 4ND-CNxNT decorated with scandium is indeed favorable. Simulations indicate that the total amount of adsorption is directly related to the operating temperature and pressure. The number of absorbed hydrogen molecules almost logarithmically increases as the pressure increases at a given temperature. The total excess adsorption of hydrogen on the (Sc/4ND)10-CNxNT arrays at 300 K is within the range set by the department of energy (DOE) with a value of at least 5.85 wt%.

Fire incidents are costly occurrences that may be preventable. This study aims to analyze fire data in the City of Manila from 2011 to 2014 based on the various causes of fires. Temporal analytical techniques complemented by geo-mapping are used to determine fire patterns based on time, day, month and year. A total of 2,316 fire incidents were included in the study and fires due to faulty electrical connections occurring from 6PM to 9PM emerged as the time with the most number of fire incidents. The daily pattern does not show much variation although the monthly pattern shows that the summer months have the more number of fire occurrences with faulty electrical connections as the main cause. The yearly pattern also do not offer much variation though the same cause of fire is noted to have the highest occurrence. Patterns identified may be useful inputs in formulating proactive fire preventive measures and in allocating fire resources. Future research directions in spatial and spatiotemporal analyses have been identified.

Self compacting concrete (SSC) is one of the most useful innovations in concrete technology that has the ability to flow efficiently and maintain material homogeneity. However, additives particularly admixtures introduced in the production of SSC to enhance some specific properties of fresh and hardened concrete may contribute undesirable effects on the workability performance. In this study, superplasticizers blended with fly ash was used in the mix and were tested for Slump Flow, L-Box, and Screen Stability tests to determine its influence on the rheological properties of SCC. Several mixtures were tested in order to derive a mix proportion having the optimum rheological properties. Artificial neural network and genetic algorithm were used to determine the concrete mix proportion that will provide the best workability. Results showed that ANN was able to establish the relationship of rheology to the concrete material components and GA derived the optimum proportion for best rheological performance.

One of the most useful innovations in concrete technology is Self Compacting Concrete that has the ability to flow efficiently and maintain material homogeneity. The rapid change in the behavior of concrete due to accelerating admixtures can significantly affect the workability properties of the mixture and reduce its ability to flow efficiently. To describe the influence of superplasticizers blended with accelerant on the rheological properties of SCC, several mixtures were tested for Slump Flow, L-Box, and Screen Stability tests. Artificial neural network was used to obtain a model describing the constitutive relationships between the material components and workability parameters of SCC and was optimized using Genetic Algorithm. Results showed that ANN was able to establish the relationship of rheology to the concrete material components and GA derived the optimum proportion for best rheological performance. Most of the design samples of SCC with blended superplasticizer and sodium lignosulfate accelerant were not able to perform well in the flowing ability due to inefficiency of the fresh SCC to flow. The increasing dosage of accelerant however rendered strong stability between the concrete particles allowing the SCC samples to resist segregation and maintain material homogeneity.