The use of Technology-Enabled Learning (TEL) has become increasingly popular in higher education as a teaching and learning methodology. TEL has been acknowledged for its various benefits, including increased flexibility and convenience for both educators and students. However, integrating TEL into the classroom setting also presents unique challenges. Despite the importance of understanding these challenges, limited research has been conducted from the perspectives of various stakeholders. Using a mixed-methods approach, this study addresses the need for a more comprehensive understanding of the challenges associated with the use of TEL in higher education. The findings show that these challenges include self-regulatory, technological literacy and competency, student isolation, technological sufficiency, technological complexity, learning resource, and learning environment. The results of this study provide a foundation for developing a technology management framework that considers the needs and perspectives of students. The challenges and potential solutions presented in this research can be used as a practical guide for managing the use of technology in the classroom effectively. By considering the challenges of TEL and incorporating practical solutions, educators can optimize the use of technology in their teaching practices and improve student learning outcomes. Overall, this research contributes to the literature by providing a comprehensive understanding of the challenges associated with TEL in higher education and practical solutions for managing the use of technology in the classroom. The findings can be used by educators and institutions to enhance their understanding of TEL and to develop effective strategies for implementing and managing technology in the classroom.

Fiber-reinforced polymer (FRP) is an innovative material in the construction industry. It is beneficial because of its toughness, and unlike steel, it is not prone to corrosion. Some research studies focus its behavior as a reinforcement in concrete while deriving several equations pertaining to its shear strength capacity. This study used the artificial neural network modeling technique to derive a more accurate solution to predict concrete shear capacity with FRP as reinforcement. Experimental data from previous studies were collected and used to train the model. The parameters considered were compressive strength of concrete, FRP ratio, beam dimensions, and modulus of elasticity. As a result, the model consistently provides a better prediction of the shear capacity of concrete against existing models like ACI 440.1R-03, ACI 440.1R-06, and El-Sayed. Furthermore, the ANN model showed no sign of disarray in predicting every parameter compared to other existing models. According to ACI 440.1R-06, FRP bars largely affect the total shear capacity of concrete. In the model provided by ACI, FRP reinforcement’s axial stiffness accounts linearly to the shear strength capacity of concrete. Since then, the predicted capacity in accordance with the ACI was excessively conservative. With respect to the derived model, axial stiffness offered a variation in the shear capacity. The proposed ANN model can be utilized for the design since the minimum ratio between the actual test result yields to 0.77 which is greater than the strength reduction factor of 0.75. Parametric studies were also conducted to show the effect of the modulus of elasticity of FRP, FRP ratio, and beam dimensions on the shear capacity.

The BS Mechanical Engineering Program of FEU Institute of Technology designed undergraduate thesis courses for its first batch of BSME students enrolled in the Project Study 1 and Project Study 2 courses. These courses were designed to be in line with governmental and institutional requirements, outcomes-based education, and optimized research output. The courses were successfully implemented and used as optimal venues for assessing program outcomes for accreditation purposes and the continuous quality improvement process of the BS-mechanical engineering program.

Due to the hardships brought about by the pandemic, Filipino farmers resort to creative ways of drying fruits. Since there is limited movement during the pandemic brought about by restrictions, farmers have no choice but to employ fruit drying on the roof of their houses to preserve the food. Currently, this is done at the mercy of the elements. However, modern fruit-drying requires monitoring of temperature and humidity so that proper measures can be taken when it is about to rain. Otherwise, rain can cause food spoilage which would cause farmer’s wastage of already scarce resources. The crop chosen for this study was Spondias sp. However, a minor adjustment can also be applied to other fruits, vegetables, and even meat. Once the drying process is complete, the farmer will receive a notification so that the dried product may be processed for packing and selling. Results indicate that the prototype can meet the specifications and that farmers found it helpful. For future works, it is recommended that solar panels be used to utilize the power from the sun. It is also recommended that direct current be used in future project modifications to minimize errors during a power outage.

Electricity, since its introduction, has been vital to inventions and development to the improvement of the quality of life of people. However, mishandled and/or unattended electrical connections and devices may cause hazardous events. The study is for the development of a smart IoT device to monitor the power and management system. The accompanying mobile application can be used to read and monitor the parameters in the smart outlet in real-time and be used to turn on or off the device remotely as long as internet connectivity is available in the smart outlet and the mobile device with the mobile application. This study aims to incorporate the advancement of automation and real-time monitoring of electrical outlets, providing additional safety measures for appliances and other machines.

An attachable exoskeleton in backpacks for postural correction by using pressure sensors has been designed and implemented. The system determines the changes in body posture and the selsyn control for the proper posture of shoulder and spine correction. The design of the prototype is based on an exoskeleton which is a backpack, having straps with a selsyn motor, pressure sensors, and portable battery or power bank. The system's functionality and reliability were tested by recording the slouching angle of five test subjects. The study also aims to monitor the improvement of posture in the duration of 2 weeks upon the usage of the system. Pre-test and post-test of posture were evaluated by the physical therapist for improvement in posture and showed a significant reduction of the number of slouches per day for the five test subjects. The four test subjects failed to reach more than 40% reduction of number of slouches, but one subject attained 39.4% reduction. The system is an active device in reminding the subjects to maintain proper posture while most of the posture correctors available in the market are passive. The prototype was able to accomplish what it was designed and created for, which is to reduce the number of slouches for the improvement of the user's posture.

Technological advancements and modernization of different industries and disciplines contributed to more consumption of oil and electricity which powers these industries. Aligned with the United Nations (UN) Sustainable Development Goals (SDG), the use of alternative and renewable energy (RE) sources is encouraged as it allows the utilization of clean energy resources and access of populations in developing countries to electricity and energy. Forecasting and maximizing the harvest for renewable energy requires an understanding of the mechanics behind the variables that impact solar photovoltaic production. 755 datasets were created from 150 days of recorded data and used in the model building and sensitivity analysis. The approach used in this study to identify the variable importance of each meteorological variable to the solar photovoltaic (PV) production was the Garson’s algorithm (GA). In this study, an artificial neural network (ANN)-based sensitivity analysis (SA) using Garson’s algorithm (GA) was implemented to identify the relative importance (RI) of the factors influencing the solar PV output including the solar irradiance (SI), rainfall, maximum temperature (MaT), minimum temperature (MiT), relative humidity (RH), and wind speed (WS). The model also considers the relative significance of these parameters to the solar PV output. Results indicate that, with a relative value of 29.48% and 5.01%, respectively, solar irradiance and wind speed are the most and least important factors.

Wind power is a challenge in power generation. The tortuous process stages in generating voltage become a significant problem to be solved properly. One indicator of the process is the determination of the right wind speed because it always changes at any time and under circumstances. For this reason, accurate predictions are needed so as to maintain the smooth integration of wind power into the overall system. Machine learning is used as a promising approach to dealing with wind intermittent power because wind speed prediction methods have been developed in recent years. This study explores climate patterns in the Philippines using data collected from PAGASA. The data is trained and tested with a machine learning model to predict wind speed. This research resulted in the Gaussian Process Regression (GPR) model outperforming other models and is very suitable for datasets in achieving accurate and reliable predictions.

While several content management systems (CMS) and audience analytics tools are available for digital signage in the market, they are often sold separately and can be expensive. Therefore, this project aims to design a cost-effective and unified web-based solution for digital signage that combines content management and audience analytics functions, reducing the need for multiple purchases. This can be achieved by utilizing Raspberry Pi technology, known for its cost-effectiveness and versatility in integrations, along with a face recognition camera and machine learning methods. This proof of concept demonstrates the integration of these components to create a dynamic digital signage system. Overall, this project has the potential to offer an affordable solution for companies aiming to efficiently manage and optimize their digital marketing strategies, especially in the Digital Out-of-Home (DOOH) Advertising space.

Relative humidity is an important environmental parameter and is widely used in various fields. Prediction of humidity levels is crucial for climate modeling, heat stress, air quality forecasting, and public health. Machine learning techniques have shown potential for predicting humidity due to their nonlinear nature. However, there is a research gap in humidity prediction in the Philippines, specifically the lack of studies utilizing the available parameters provided by PAGASA, presenting an opportunity for further investigation and development of models for predicting humidity levels in the country. In this study, the researchers used a publicly available dataset from PAGASA containing weather measurements from 2000 to 2022 in the Philippines. Various machine learning models were trained and tested, with hyperparameter tuning performed using Bayesian optimization. The Gaussian Process Regression model with optimized hyperparameters achieved the best performance in predicting relative humidity, with the lowest RMSE and highest R-squared values. This study provides a reliable way to predict humidity levels in the Philippines based on weather parameters.