De La Salle University

About: De La Salle University is a education organization based out in Manila, Philippines. It is known for research contribution in the topics: Population & Computer science. The organization has 2951 authors who have published 4374 publications receiving 49567 citations. The organization is also known as: Pamantasang De La Salle.

Looking at Filipino Pre-Service Teachers' Value for Education through Epistemological Beliefs about Learning and Asian Values.

Abstract: The present study investigated the contribution of epistemological beliefs about learning and Asian values on pre-service teachers’ value for education. The relationship of epistemological beliefs and valuing education is based on Schwartz and Bilsky’s (1987; 1990) theory of human values. The participants were 362 pre-service teachers from different universities in Manila, Philippines. It was found in a standard regression analysis that both complexity and structured belief about learning significantly predicted valuing one’s education. Higher expectation for achievement, filial piety, and emotional restraint were also significant. A hierarchical multiple regression was used to test the order of contribution of Asian values (filial piety, hierarchical family structure, collectivism, emotional restraint, perseverance and hard work, higher expectation for achievement, respect for authority, and respect for education) on valuing education. The results showed that the order of Asian values based on the theory of Schwartz and Bardi (2001) significantly explained the variation on valuing education (Adjusted R 2 =.35). The contribution of each Asian value increased the variation on valuing education but it remained stable with the inclusion of respect for education and respect for authority.

Fuzzy analytic hierarchy process (FAHP) for multi-criteria selection of microalgae harvesting and drying processes

Abstract: Microalgae are considered to be a promising source of biomass compared with first and second generation feedstocks. However, the high energy requirement for harvesting and drying of the algal biomass poses challenge to commercialization due to implications on both carbon footprint (CF) and cost. In this work, we propose a systematic methodology for the multi-criteria evaluation of alternatives for the harvesting and drying processes. A fuzzy analytic hierarchy process (FAHP) approach is used, where the pairwise comparison of the multiple criteria and alternatives were done to prioritize the best harvesting and drying method within the fuzzy bounds of the value judgment that satisfies the consistency index. FAHP also allows the degree of confidence of the expert to be quantified. A case study of four alternatives each for the harvesting and drying process is used to demonstrate the process. Technology capability, cost and environmental impacts (comprised CF, land footprint and water footprint) are identified as the selection criteria for harvesting and drying process, respectively. Results show that flotation is the best alternative for harvesting process, while sun drying is the best among the drying alternatives. Sensitivity analysis is used to give insights on the robustness of the decision model and enables the understanding of critical criteria that would significantly influence the ranking of the alternatives. The proposed FAHP approach therefore can effectively deal with the uncertainty of judgment in the decision-making process in the evaluation of microalgae harvesting and drying processes.

Shed: An Online Diet Counselling System

Abstract: In today's health conscious (or lack of health consciousness) society there is a need for a more personalized, tailor-fit solution for maintaining a healthy diet for users. There are already existing recommendation systems aimed at helping users maintain a healthier diet. However, there is no single software application which is capable of combining all the capabilities (i.e., diet information, recipe recommendation, and recipe information) and thus there is no consolidated source of assistance or reference. Furthermore, there is a lack of recommendations presented in natural language that are produced by existing systems which means that outputs are not yet tailor-fit for users. Because recommendations do not highlight the benefits of choosing appropriate foods, the users may therefore be given irrelevant information or incomplete information that could have motivated them to choosing a healthier meal. This paper discusses Shed, our diet counselling system, that combines all the essential facilities of existing systems and provides live, on-the-fly healthy diet plan recommendations that are presented in natural language which would be aimed directly at the user's needs and interests. Shed generates personalized meal recommendations by identifying which recipe (available on-line) that promotes weight loss and maintenance. The basis for the recommendations is on the basal metabolic rate (BMR) of the user which determines the calories he/she must take for a day. Also included in the paper is an overview of the data needed in the processing and the components that comprise the system. Lastly, we present some initial results from user acceptance test.

Neural network-based path loss prediction for digital TV macrocells

Abstract: Path loss prediction in radio wave propagation models are often categorized as theoretical/physical, empirical or a hybrid combination. Theoretical propagation models rely more on the physical behavior of radio waves while empirical models are based on actual field strength measurements in a particular environment. Consequently, the equations for theoretical models are based on physics while those for empirical models are based on statistical analysis of the gathered data. While physical models can be adapted to any type of environment, they are known for their computational complexity since they consider the path profile to each and every point in a given area. Empirical models are attractive for their computational efficiency but they may only be suited to the specific place where the measurements were conducted. This paper aims to propose and ascertain the viability of using an alternative neural network (NN) model to predict path loss. The approach may be loosely categorized as empirical since actual field strength measurements should be basis for the prediction model. For purposes of preliminary analysis, however, the field strength measurements will be replaced with results from a Longley-Rice model simulation, which is often used for TV transmission. The simulation data will be used to generate training and validation for the neural network. Once neural network fitting using feedforward backpropagation is achieved, the neural network-based propagation model is shown to give more accurate results compared to more familiar propagation models, such as Free Space and Egli, while having the advantage of adaptability to arbitrary environments.