Oliver A. Medina

Malayan Colleges Laguna

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Mary Grace P. Beano

Malayan Colleges Laguna

Aldrin J. Soriano

Technological University of the Philippines

Anna-Liza F. Sigue

Malayan Colleges Laguna

Maria Emmalyn Asuncion Capuno

Ericson A. Mandayo

Paul Michael M. Mondonedo

Hannah Faye A. Orence

Carl Joseph Mantilla

Juan Paulo E. Migrino

Edryl Arden Bolibol

Malayan Colleges Laguna

Cooperative Institutions

Malayan Colleges Laguna

National Training Service

Serviço Nacional de Aprendizagem Comercial

Center for Research and Advanced Studies of the National Polytechnic Institute

Universidad Nacional Autónoma de México

Asociación Mexicana de Psicología y Desarrollo Comunitario

Instituto Tecnológico Superior de Irapuato

Instituto Politécnico Nacional

Instituto de Estudios Avanzados

Toronto Metropolitan University

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Human mobility patterns in Mexico City and their links with socioeconomic variables during the COVID-19 pandemic

arXiv (Cornell University) (2022)

Óscar Fontanelli Dulce I. Valdivia Guillermo Romero Oliver A. Medina Wentian Li

The availability of cellphone geolocation data provides a remarkable opportunity to study human mobility patterns and how these patterns are affected by the recent pandemic. Two simple centrality metrics allow us to measure two different aspects of mobility in origin-destination networks constructed with this type of data: variety of places connected to a certain node (degree) and number of people that travel to or from a given node (strength). In this contribution, we present an analysis of node degree and strength in daily origin-destination networks for Greater Mexico City during 2020. Unlike what is observed in many complex networks, these origin-destination networks are not scale free. Instead, there is a characteristic scale defined by the distribution peak; centrality distributions exhibit a skewed two-tail distribution with power law decay on each side of the peak. We found that high mobility areas tend to be closer to the city center, have higher population and better socioeconomic conditions. Areas with anomalous behavior are almost always on the periphery of the city, where we can also observe qualitative difference in mobility patterns between east and west. Finally, we study the effect of mobility restrictions due to the outbreak of the COVID-19 pandemics on these mobility patterns.

Pandemic

Geolocation

10.48550/arxiv.2208.06456

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Azolla Quantity Prediction using WSN and Machine Learning

TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON) (2022)

Bernadette Aala Edryl Arden Bolibol Mark Jameson Caoile Carl Glenn De Leon Geline Refrea

Due to COVID-19 pandemic, the expenditures on pellets and feeds in broiler and fish industries increase every year, leading to price overshoots in various agricultural products. Azolla is an emerging protein source alternative for tilapia and other livestock breeders that is known for its fast reproduction. This study aims to enhance the yield production of Azolla ponds in Nevalga Farm, Brgy. Sala, City of Cabuyao, Laguna by employing wireless sensor network (WSN) technology and predictive machine-learning (ML) methods. LoRa-based WSN was designed to measure the parameters that affect the growth and reproduction of Azolla. Throughout the 24-day monitoring period, the average received signal strength indication (RSSI) and signal-to-noise ratio (SNR) of the packets from the three sensing nodes ranged from −50.86 dBm to −71.39 dBm and 8.92 dB to 9.81 dB, respectively. A total of 3582 data sets were obtained during the observation. Among the three regression ML models used, K-Nearest Neighbor algorithm outperformed Linear Regression and Support Vector Machine in predicting Azolla quantity parameters on both training and validation datasets by yielding the smallest values of root mean square error (RMSE) and absolute error on the seven quantity indicators and achieving squared correlation that varied from 0.935 to 0.997.

Azolla

10.1109/tencon55691.2022.9977721

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Lettuce Root Development and Monitoring System Using Machine Learning in Hydroponics

TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON) (2022)

Bryan B. De Los Santos Anna Grace M. Don Angelito A. Gotengco Jose Luis M. Millena Rhaizel Gel C. Romero

Roots of a hydroponically grown lettuce are usually susceptible to waterborne diseases like Pythium disease (root rot). Visual inspection of a plant root is needed in a hydroponic system to determine the health status of the plant. However, removing the plants in its growing environment will stress the plants that can cause stunted growth, and is susceptible to further complications once the roots are exposed outside the environment. Also, studying the development of the lettuce roots on its freshness could help to set standards in determining its shelf life and could lead to a longer life. The project intends to develop an automated root monitoring system for detecting root rot and changing water temperature to avoid extreme heat that damages the root system, affecting the entire plant. The Peltier module was employed to keep the water temperature stable between 18°C – 26°C. If the water temperature exceeds this range, the system will react based on what is best for the plant. The researchers used a camera to monitor the roots across the three grow boxes for the root detecting method. The researchers also implemented image pre-processing, specifically cropping and resizing, to improve image quality in preparation for image processing, and the Decision Tree method for machine learning. The model detected root rot with 91 % accuracy. The system was able to accurately identify and locate roots as well as predict their health status.

Hydroponics

Root system

Root (linguistics)

Root rot

DNS root zone

10.1109/tencon55691.2022.9977465

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Web-based Riverbank Overflow Forecasting and Monitoring System

Ma. Vienna R. Lozanes Carlo C. Nunez Ricardo O. Zapanta Aldrin J. Soriano Mary Grace P. Beano

River overflowing is a common problem in the Philippines. And knowing the fact that we lack proper devices and systems to monitor the water flow makes it even more disturbing. Therefore, this project is to develop a prototype that will measure, monitor, and forecast the water level and the volume of water flowing through the riverbanks. The proponents used Agile development method includes systematic process of designing, developing the prototype, evaluating instructional programs, and system that must meet the criteria of the effectiveness of the project. The prototype consists of algorithms that is performed to have a reliable data. In measuring the river parameters, Wireless Sensor Network (WSN) Algorithm is used to interconnect the sensor nodes in different area of the riverbank. In forecasting, the device performed a machine learning by gathering raw data to run in Voting Algorithm. In monitoring, the prototype provides Cabuyao River Monitoring System (CRMS) a web-based application that uses a Decision Tree Algorithm that will give information and warning to the community. This device and system aim to give early warnings to the communities being both in a timely and systematic way, providing advanced information on its behavior to better prepare the disaster team and government.

10.1109/tencon50793.2020.9293830

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ANÁLISIS ESTADÍSTICO DE FACTORES DE PERSONALIDAD DE LOS APRENDICES CEFA MEDIANTE LA PRUEBA 16PF

Revista Rutas de formación prácticas y experiencias (2018)

Oliver A. Medina Cristian Chávarro Viviana González Yuleidy Vásquez Ramírez

Considering Cattell's thesis, it is possible to affirm that the personality allows a prediction of what a person will do in a given situation, while the traits are the personality units with predictive value: what a person will do when faced with a given situation; they are abstract concepts, useful tools for predictive purposes, but not necessarily correspond to a specific physical reality. Within the process of Integral Vocational Training of SENA various extrinsic and intrinsic personality factors intervene and, to a certain extent, influence the different learning styles of each individual when facing the training itself. For the present investigation, we intend to analyze the intrinsic factors of personality, through the Cattell 16PF Personality Factorial Questionnaire, which allowed the analysis of 16 personality factors that influence human behavior and, therefore, learning.

16PF Questionnaire

10.24236/24631388.n7.2019.1924

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IoT-Based Growth Analysis of Red Onion in Controlled Hydroponic Environment using Electroculture

TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON) (2022)

Nicole Andrei Basallote Mary Anne Gardones Carl Joseph Mantilla Dean Andray Masaya Jet Trick Sol

Hydroponics has grown in popularity in recent years due to its ability to create nutrient-rich crops with minimal inputs. The project focuses on creating a hydroponic system that uses internet of things technologies and electroculture to give a variable growth analysis. The study describes a current system for monitoring total dissolved solids, pH, and photosynthetically active radiation, leaf number and length in the Web application platform using Wi-Fi. The old horticulture method called electroculture which supplies certain amount of voltage that can stimulate plant growth and increase crop quality as long as the factor contributor are present and correct. The onion is planted in the hydroponic system which composed of 8 set-ups of voltages ranges from OV and 3V to 9V. The effects of the provided voltage on plant height were then visualized. The reaction of onion (Allium cepa) to voltage application was adverse - the average height is significantly higher for the neutral system. The investigation shows that in neutral voltage, the increase of leaves height per day ranges from 0 to 1.7 cm with a longest length increase of 1.7 cm in just one day. Dried and decayed leaves are also visible at the end of the month of experimentation period.

Hydroponics

10.1109/tencon55691.2022.9977614

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Controlled Environment for Spinach Cultured Plant with Health Analysis using Machine Learning

2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) (2021)

Mdas Angelo M. Araneta Daniel V. Asenjo Carl Jeremiah L. Lamprea Argilyn Mae L. Reyes Oliver A. Medina

The combination of mobile application and machine learning with environmental sensing and image processing device in monitoring the health and growth of the plant in real-time. The goal of the study is to develop a controlled environment for spinach by monitoring its health condition using image processing and supervised machine learning. The device collects real-time data for humidity, temperature, and soil conditions using different sensors. Diagnosis of spinach health status is done by capturing the images of spinach using image processing techniques. Two classifiers were used in detecting spinach health conditions, Green for healthy spinach or no damages on leaves and Green, Yellow, Brown for unhealthy or with holes, damages; this classifier is also called datasets. Spinach health status is monitored using a mobile application called Spinach Monitoring Application (SPIMON) and all collected data are stored in the cloud. The result spinach health status showed that its best to culture spinach in a controlled environment.

10.1109/hnicem54116.2021.9732020

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IoT-based Assessment and Monitoring of NPK Content and Fertility Condition of Soil

TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON) (2022)

Jeremy Aliparo Christian Delos Santos Chrissa Mae Sese John Ver Trinidad Mary Grace P. Beano

Since infertile soil is abundant on Philippine land, farmers need to first know the condition of the soil before planting crops on it. The key to good crop production is providing plants with the proper amount of nutrients at the appropriate time. Farmers who are not exposed to contemporary technologies continue to use traditional methods to enhance yield production, which is no longer a highly productive sort of farming. This study aims to create a system that will help farmers and home gardeners assess and monitor the condition of their crops by checking the variables of the soil such as NPK (Nitrogen, Phosphorus, Potassium) content, moisture level, acidity level, and humidity with the help of Internet-of-Things (IoT), which allows farmers to get fast insights on issues before they happen and make informed decisions on how to avoid them. The system also tells you which crop is best for your soil based on how much NPK it has. This information comes from the result retrieved from sensor that this study will use. The researchers used Evolutionary Prototyping in developing the Sensoil Device for the purpose of making the SAMS (Soil Assessment and Monitoring System) system as user-friendly as possible by implementing what the experts and user respondents wanted. The results show that with the implementation of the device, crop growth becomes healthier and faster, and it increases the potential to have a higher yield production that will boost the income generated from the farmlands while reducing unnecessary fertilizer and water use.

10.1109/tencon55691.2022.9978040

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Automated Aquaponics System and Water Quality Monitoring with SMS Notification for Tilapia Industry

Ma. Jenica M. Autos Samantha Kaye S. Falculan John Jeremy A. Fortin June F. Mendoza Anna-liza F. Sigue

This paper introduces the development of monitoring and maintaining optimal water quality in an aquaponics system. The design is based on the hydroponics system's Nutrient Film Technique (NFT) in which plant roots are being exposed to a thin layer of nutrient water in a long narrow channel. The design used a developmental and experimental type with the use of microprocessors and sensors for enhanced monitoring and error-correcting within the aquaponics system where standard data was obtained from different reliable sources. Various sensors are calibrated for different measurements to provide accurate and reliable readings of water temperature, pH level, dissolved oxygen, total dissolved solids, water flow, and temperature and humidity. The Arduino Mega reads and analyzes data collected by various sensors, and instructs actuators such as aquarium heater, cooling fan, aerator, grow light, and water pump. The data gathered appears on the built-in LCD screen and will be sent to the owner's mobile phone regarding the condition of the system. Also, the system has a fish feeder that automatically dispenses food at a given time. The device can be controlled wirelessly using a mobile phone and manually using a 4x4 keypad. Also, the owner can monitor the way each actuator was controlled.

Aquaponics

Keypad

Arduino

Mobile phone

10.1109/tencon50793.2020.9293868

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Tracelet: Contact Tracing and Physical Distancing Device with Block-chain Technology for Pamantasan ng Cabuyao

2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) (2021)

Daniela Luanne V. Abo Mary Anthoinette B. Amorin Harold T. Alcabasa Juan Paulo E. Migrino Aldrin J. Soriano

In the Philippines, contact-tracing is done in the conventional method where people fill-up health-declaration forms. Proper physical distancing is not followed, especially in public places. This study is focused on concerns involving the conventional contact tracing and physical distancing in Pamantasan ng Cabuyao. Developed using modified incremental-prototyping methodology. The objective is to develop module device with ranging position, triggered when one-meter distance is compromised and physical distancing notification. Included a web-based application interface for contact-tracing where the administrator can update the status and registration of the users. Utilized block-chain technology as storage for the users' information. The device is developmentally-designed, implemented and evaluated. Based on the series of testing, the device provides notification through blinking light, and set-off to exchange identifications. Users' information is secured through the Block-chain. When a user is reported to be COVID-19 positive, all the users who have been in contact, will be alerted via text message.

Distancing

Contact tracing

Tracing

10.1109/hnicem54116.2021.9731916

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