J.H. Cerilles State College

Intercropping, the practice of cultivating two or more crops simultaneously on the same land, is a proven strategy to enhance land use efficiency, reduce pest pressure, and improve overall farm productivity. This study evaluated pest infestation, soil biodiversity, and productivity of corn (Zea mays), mung bean (Vigna radiata), and conditions across two cropping seasons in Bagong Oroquieta, Guipos, Zamboanga del Sur, Philippines. Eight cropping patterns (CP1-CP8) were tested. Major insect pests aphids (Aphis craccivora), and sweet potato weevil (Cylas formicarius). Results revealed that CP5 (Mung bean-Corn), CP6 (Sweet potato-Corn), and CP8 (Sweet potato-Corn + Mung bean) consistently minimized pest infestation, maintained weed populations at tolerable levels, and supported a favorable soil biodiversity index. Productivity was assessed using the Land Equivalent Ratio (LER), an index that quantifies the relative efficiency of intercropping compared to monocropping. The corn + mung bean combination produced the highest LER values (1.30-1.53), indicating a 30-53% yield advantage over monocropping and the most favorable competitive ratio. The findings underscore the potential of mung bean-corn and sweet potato-corn-based intercropping systems under coconut to enhance farm productivity, reduce pest incidence, and promote ecological sustainability. The study recommends these patterns to coconut farmers for improved income and resource utilization.

Infectious disease forecasting remains difficult in public health surveillance due to heterogeneous epidemiological data that may not always be available in developing countries. Several disease surveillance systems are currently being conducted by the Department of Health in the Philippines, including the Philippine Integrated Disease Surveillance and Response (PIDSR). Yet, the data obtained from such surveillance systems are incomplete, inconsistent, and have low temporal granularity, which makes their use for feeding into machine learning models inappropriate due to the lack of a proper time-series data structure. To address this problem, this study presents a temporal representation learning approach using self-supervision, called DOH-SSL, for multivariate epidemiological time series analysis. This model can learn informative representations from raw, unlabelled surveillance data using masking and temporal augmentation. Experiments using this model and other baselines (ARIMA, XGBoost, LSTM, Transformer) on the tasks of forecasting and classification showed that DOH-SSL outperformed all of them.

Adequate monitoring of flour storage bins in the food manufacturing industry can prevent profit loss from underproduction and overstocking. Manual volume measurement is labor-intensive and error-prone. With the need for efficient monitoring in mind, this study presents the design and development of volumetric measurement of the flour inside a storage bin using 2D-based rotating LiDAR to capture 3D point cloud data. The proposed system eliminates manual probing by fully automating the scanning and volumetric computation workflow. Instead of relying on discrete physical measurements inside the bin, the 2D rotating LiDAR continuously captures the interior walls and flour surface to generate a dense 3D point cloud. This removes the need for operators to insert rods or probes and thereby avoids human-induced measurement variability. Furthermore, because the system computes flour volume directly from geometric reconstruction rather than converting probe depths using a uniform surface assumption, it does not rely on a constant material density and is therefore more robust to compaction differences within the bin. The high-resolution point cloud also generates accurate mapping of non-uniform and irregular surface geometries, which captures true depressions, peaks, and sloped regions that manual methods typically miss. A dedicated web application was developed to send commands to the system for automated scanning and real-time volume computation. Successful real-world testing showed the system’s reliability, with an accuracy level of 1.013 ± 0.70% MAPE across varied flour quantities and surface contours.

Community-Based Rehabilitation (CBR) is increasingly positioned as a cornerstone of restorative and community-oriented corrections, yet its implementation is often assessed through offender outcomes rather than through the frontline officers who translate policy into practice. In the Philippines, empirical knowledge remains limited on how Probation and Parole Officers (PPOs) experience, negotiate, and sustain CBR work amid institutional constraints. Addressing this gap, this qualitative case study examined the lived experiences, implementation challenges, and coping mechanisms of PPOs in Region IX, Zamboanga Peninsula. Data were generated from ten experienced PPOs through in-depth interviews and focus group discussion and analyzed thematically. Findings reveal that PPOs construct CBR as both a rehabilitative duty and a moral vocation, deriving professional meaning from client transformation and successful reintegration. However, their practice is constrained by heavy caseloads, inadequate operational resources, long-distance field assignments, organizational tensions, shifting policies, and recurring ethical dilemmas. Despite these pressures, PPOs manage their work by continually re-prioritizing, managing emotion, learning, coping, and public service. The unique contribution of this study is conceptualizing CBR implementation as a human-institutional practice – rather than as a policy or client-driven practice – that is affected by the well-being, discretion, and coping capacity of officers. The study's theoretical contribution is to restorative justice and community corrections scholarship by revealing that rehabilitative results depend not only on laws and client obedience, but also on officers' emotional labor and on formal institutions' support mechanisms. The results provide directions for evidence-based improvement of support mechanisms for officers, resource distribution, and the sustainability of CBR implementation in the Philippines, and in similar correction systems.

Function art is an art form that uses graphs of mathematical functions in creating artwork. It integrates mathematical functions, art, and technology within the STEAM framework. Despite its potential, a standardized method for evaluating function art remains lacking. This study develops and validates a rubric based on the Torrance Test of Creative Thinking (TTCT) to assess function art, focusing on Fluency, Flexibility, Originality, and Elaboration. This paper explores the development and validation of this rubric in an educational context. The rubric was designed for our specific setting, but it can be generalized. To create this rubric, the researchers adapted the Torrance Test of Creative Thinking. The rubric was tested on 51 function art pieces created by Grade 11 students in the Philippines. The psychometric evaluation of the function art task using Rasch measurement revealed robust reliability and validity. Correlation analysis identified a positive relationship between Fluency, Flexibility, and Originality. Regression analysis highlighted significant reciprocal influences between Fluency and Originality. Findings suggest that the framework can be used to objectively get a score on a single piece of artwork. The study contributes to the pedagogical landscape by providing educators with a tool for evaluating function art.