Organizations are challenged to achieve effective and competent results, rising to imminent importance of measuring the performance efficiency. Data Envelopment Analysis (DEA) is an approach that measures performance efficiency of organizations. It is a non-parametric method, which uses linear programming to calculate efficiency in a given set of decision-making units (DMUs). It has widespread application in identifying efficiency and discovering benchmark. In the study, it utilized DEA in identifying School-Based Management (SBM) performance efficiency of one (1) division comprising of elementary and secondary schools - under Department of Education (DepEd) in the Philippines. Efficient schools were used as benchmark for improvement of inefficient schools. The schools had also undergone clustering, which is the process of grouping in accordance to similar characteristics. K-Means clustering algorithm was used to group the schools according to their respective profile. K-Means clustering is a simple unsupervised learning algorithm that follows a simple procedure of classifying a given data set into a number of clusters. The study also encompasses the development of an application system that utilizes data from DEA and K-Means clustering algorithm. The application system also provided recommendations to help inefficient schools improve.

A trigeneration system consists of interdependent technologies for power generation, heat generation, and the generation of cooling effect which leads to the overall improved thermodynamic efficiency of the system. However, the optimal design of a trigeneration system also depends on the product price variability of the energy streams which are highly dependent on the price of the raw materials and the product demand. Taking into consideration expected price fluctuations of streams, it is then possible for plant owners and engineers to evaluate the investment risk associated with the design capacity of a trigeneration system. The study proposes the use of a 2k factorial design of experiments together with fuzzy linear programming to conduct a price sensitivity analysis in the optimal design of a trigeneration system. This type of analysis can provide plant owners information on possible configurations for optimal capacity given uncertainty in the price parameters.

The production of fiber-reinforced concrete presents a complex reaction system, posing significant challenges in determining appropriate material proportions to achieve targeted mechanical properties. To address this issue, this study proposes novel Artificial Neural Network (ANN) models for predicting the compressive and flexural strengths of fiber-reinforced concrete using a backpropagation feed-forward algorithm. A wide range of concrete mix designs was prepared and tested using cylindrical samples for compressive strength and beam samples for flexural strength. Polypropylene fibers were incorporated into the mixes, and all specimens were cured for 28 days in a water-saturated lime solution. The results demonstrated that the ANN models produced strength predictions that closely aligned with experimental data, yielding high correlation values of 99.46% and 98.57% for compressive and flexural strengths, respectively. The best-fit models exhibited mean squared errors of 0.0024 (compressive) and 0.44 (flexural). Furthermore, parametric analysis indicated that the proposed models effectively captured the constitutive relationships among the concrete components and successfully represented the dominant mechanical behavior of the tested specimens.

A trend arises among industrial and government sectors to transition from the conventional economic system to the new Circular Economy. Its benefit of material security, resource efficiency, and economic growth has attracted government institutions and business sectors to adopt the new trend. However, its challenge falls on the real complexities of economic systems. Adoption of the Circular Economy requires careful consideration of possible challenges. Previous works have aimed to identify the drivers of Circular Economy through surveys based on the frequency of data. The results provided useful information for the decision making of the transition. However, it is also limiting as it does not address a plausible chain-like effect of the drivers which can aid stakeholders determine which course of action is necessary for an efficient transition. Hence, this study is focused in determining these causal drivers by using the DEMATEL approach. DEMATEL is a methodology that identifies the cause and effect relationship between drivers, of which, it can then determine the top causal driver. The study uses a case study in the Philippines to illustrate the capability of the methodology of determining the causality between drivers of Circular Economy. The results of the case study were able to identify ‘economic attractiveness’, with a net cause/effect value of 1.22, and ‘consumer demand’, with a net cause/effect value of 0.87, as the main causal driver while ‘company culture’, with a net cause/effect value of -1.22, as the main effect. The result implies that the improvement in the circular business models and increase in customer awareness are the top priority for the transition. The application of this work is intended to provide researchers an alternative approach in identifying the critical causal drivers of Circular Economy.

Electric vehicle offers a cleaner and sustainable alternative to transportation as it eliminates direct carbon dioxide emission through the conventional internal combustion engine. With the increase in the global population and economic development, the demand for transportation and the adoption of electric vehicles is unprecedented. However, the adoption of electric vehicle on a national-scale requires long-term planning of infrastructure development, and energy generation and distribution. The study focuses on the development of a systematic mathematical programming approach in the optimal planning of the energy mix of the additional power generation capacity arising from the adoption of the electric vehicle in a developing country. The study considers the 2030 horizon which includes, the cost of power generation and distribution per energy mix, and the forecasted commissioning and decommissioning of energy plants. The study proposes a fuzzy mixed-integer non-linear programming model in the optimal planning of the energy mix for the adoption of EV while minimizing carbon footprint, minimizing the total capital cost, and minimizing the electricity cost. A case study in the adoption of electric vehicle in the Philippines will be utilized to demonstrate the capability of the model. In addition, a comparison of the electricity cost of the business as usual (BAU) scenario and this study has been evaluated. The results show that the various renewable energy technologies for power generation are selected initially from 2019 to 2022 and 2029 to 2030, while the fossil-fuel based power plants were utilized from 2023 to 2028. The results revealed the electricity cost from the study is relatively lower than the BAU scenario. The results of the model are intended to aid and guide policymakers in the potential adoption of electric vehicles, especially in the energy planning sector.

Nowadays, a lot of Filipinos are keen into traveling locally and abroad that encourages different airline companies to enhance their services to gain more clients. In this study, we extracted Twitter tweets about the three major airlines in the country today, Cebu Pacific, Air Asia, and Philippine Airlines. Various models, techniques and classifiers were introduced in computing the sentiment scores of the Tweets gathered which is useful to different fields like education and businesses. This study aims to apply a new approach in implementing sentiment analysis task thru pattern matching using a regular expression and implementing lexicon-based scoring of sentiments. Its main purpose is to assign the correct polarity to get the sentiments of the Filipino travelers.

In theory of computation, a deterministic finite automaton (DFA) is a finite state machine that accepts/rejects finite strings of symbols and only produces a unique computation. This study aims to convert the extracted decision tree rules sets from decision tree algorithm and the learning path sequence of the learning management system. This paper converts decision tree and learning management system rules into deterministic finite automaton. The decision tree rule sets are rule sets used to predict the vulnerability of not having graduation on time. The learning management system rules are the backbones of the learning management system in order to provide individualized learning scheme tailored to the preferences of the learners. The conversion of decision tree rules of student graduation and adaptive learning environment lead to easier interpretation and visualization of the methods and processes involved.

o monitor the initiation of concrete cracking beyond the service life of the structure, a novel prediction model of time to cracking of concrete cover using artificial neural network (ANN) was developed in this study. Crack mitigation prevents corrosion and crack development to occur in a more rapid phase that is an essential component in performance-based durability design of reinforced concrete structures. Data available in various literatures were used in the development of the ANN model which is a function of compressive strength, tensile strength, concrete cover, rebar diameter, and current density. The neural network model was able to provide reasonable results in time predictions of cracking of concrete protective cover due to formations of corrosion products. The performance of ANN model was also compared to various analytical and empirical models and was found to provide better prediction results. Even with limitations in the available training data, the ANN model performed well in simulating cracking of concrete due to reinforcement corrosion.

The combination of multimedia technology and Internet provides an amiss channel for pornographic contents accessible by certain sensitive groups of people. Furthermore, the same channel provides the easiest medium to distribute illicit images and videos without an autonomous content supervision process. In this study, an application was developed grounded from a pixel-based approach and a skin tone detection filter to identify images and videos with a large skin color count and considered as pornographic in nature. With nudity detection algorithm as the foundation of the system, all multimedia files were preprocessed, segmented, and filtered to analyze skin-colored pixels by processing in YCbCr space and then classifying it as skin or non-skin pixels. Afterwards, the percentage of skin pixels relative to the size of the frames is calculated to be part of the mean baseline for nudity and non-nudity materials. Lastly, the application classifies the files as nude or not, and then filter it. The application was evaluated by supplying a dataset of 1,239 multimedia files (Images = 986; Videos = 253) collected from the Web. On the final testing set, the application obtained a precision of 90.33% and accuracy of 80.23% using the supplied dataset.