Dental shade matching for composite restoration to natural teeth color is a crucial aspect of dental treatment as it can significantly impact patient satisfaction and treatment outcomes. However, the subjective nature of manual shade selection often leads to shade mismatch, which leads to failure on the first visit. In addition, intraoral scanners are inaccessible to small enterprises dental clinic in the Philippines due to its unaffordable pricing. To address this problem, this study proposed a mobile application that utilizes image processing and deep learning techniques for objective and consistent dental shade matching. Exploring Convolutional Neural Network (CNN)-based MediaPipe for Facial Landmark Detection and Support Vector Machines (SVMs) to classify dental shades. The SVM model attained an overall accuracy of 68.5% during the experimental results while the implementation using the mobile application obtained an estimate of 90% during the user testing for A1 to A4 color shade. The findings have significant implications for clinical practice, empowering dental professionals with a reliable tool to improve patient care and satisfaction. This study emphasizes the importance of incorporating advanced technology into clinical practice, ultimately improving patient outcomes.

Online learning has become fundamental to modern academic and professional development. Amidst its widespread adoption, there is increasing integration of artificial intelligence (AI) to enhance the learning experience. Understanding student engagement within these AI-powered digital platforms is crucial, as it directly influences learning outcomes and satisfaction. This chapter provides a narrative review of key theories and models essential for analyzing engagement in virtual learning contexts. Particularly, it focuses on constructivist learning theory, social learning theory, cognitive load theory, flow theory, technology acceptance model, self-determination theory, cognitive theory of multimedia learning, and feedback intervention theory. By examining these frameworks through an epistemological lens, the chapter explores how knowledge acquisition, cognitive processing, and social learning principles interact within AI-enhanced educational contexts. The insights reported here can serve as a guide for optimizing AI to maximize student involvement and educational efficacy.

The Technical Education and Skills Development Authority (TESDA) in the Philippines issues National Certificates (NCs) which is an important credential for graduates and skilled workers, affirming their capabilities in line with defined competency standards. However, with the advancement in information technology and the availability of affordable editing tools in the market raised concerns about the creation of counterfeit documents including NCs. The study focused on creating a smart credentialing and verification system for issuing National Certificates using blockchain technology. Researchers used Polygon blockchain that implements Proof-of-Stake consensus algorithm for system's efficiency and security. Certificates generated by the system are stored on the blockchain, with each certificate assigned a unique address for verification purposes. The system was assessed using ISO/IEC 25010 standards, and respondents provided good feedback on a variety of parameters. Future development recommendations include integrating a mobile application for easier certificate access and verification, providing real-time updates, establishing a feedback mechanism, and implementing analytics to gain insights into certificate issuance and user engagement.

Microplastics (MPs) have emerged as a widespread environmental contaminant, raising growing concerns about their impact on terrestrial ecosystems. This comprehensive review paper highlights the effects of MPs on soil properties, soil organisms, and plants, shedding light on the complex interactions within these critical components of terrestrial environments. In terms of soil properties, plastics, ranging from macroplastics to mesoplastics, microplastics, and nanoplastics, have been found to exert significant influence. They can alter soil physical attributes, including texture, structure, bulk density, water aggregate stability, water holding capacity, and rainwater infiltration. Microplastics can affect soil chemical properties by influencing pH levels, electrical conductivity, nutrient cycling, and enzyme activity, and even can cause heavy metal accumulation in plants. These alterations in soil properties have far-reaching implications for ecosystem health and agricultural productivity. Furthermore, microplastics have substantial repercussions on soil organisms, particularly earthworms, collembolans, and microbial communities comprising bacteria and fungi. These organisms play pivotal roles in nutrient cycling and soil health. Microplastics can disrupt their habitats, affect their behavior, and potentially lead to changes in soil biota composition, with widespread effects throughout the terrestrial food web. Microplastics influence plant growth and development; even the microplastic can be uptaken and translocated within plant tissues. Food safety and ecosystem dynamics are affected by these effects. This review paper emphasizes the urgency of understanding the complex interactions between microplastics and terrestrial ecosystems. It highlights the need for further research to comprehensively assess the extent and implications of microplastic contamination in various soil types, under different environmental conditions, and concerning diverse plastic characteristics. Standardized methodologies for studying these interactions are essential to facilitate comparisons across studies.

Abstract. This paper presents a collaborative effort to develop localized solar photovoltaic (PV) power output (PPV) forecasting models for the Philippines using geospatial data. It underlines the importance of solar energy in the country and discusses the opportunities and challenges associated with PPV forecasting. Project SINAG, a two-year research project, aimed to develop solar PV output forecasting models through a collaborative approach with academic institutions, solar energy industries, and government agencies. Actual PPV data from 43 solar PV installations were analyzed alongside meteorological data from the PAGASA weather bureau, ERA5, AHI-8, and FY- 4A. These datasets were filtered based on a one-year period to ensure quality. The study employed SARIMAX, LSTM, and XGBoost models individually and in hybrid models to develop the forecasting models. Model performance was evaluated using root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). In a case study in Baguio City, the SARIMAX model exhibited strong seasonal dependence, providing more accurate forecasts in dry seasons than in wet seasons. Additionally, the forecasting accuracy of each model (SARIMAX, LSTM, and XGBoost) varied based on the month and location of the installation, emphasizing the need for local and season-based PPV forecasting models. Despite implementation challenges, such as collaboration arrangements, bureaucratic barriers, and budget constraints, the project produced thirteen research publications and provided data for three student theses. This paper also demonstrated diverse engagements and contributions that emphasize the significance of collaborative research in conducting nationwide-scale data-driven projects.

Wind forces on structures have the potential to cause significant damage. A database involving the distance from the ridge, enclosure classification, surface type, elevation above ground level, wind direction, basic wind speed, presence of wall/surface openings, and effective net wind pressure (ENWP) was created using computation fluid dynamics (CFD). This paper focuses on the development of a model for predicting ENWP using a backpropagation-artificial neural network (BP-ANN). Utilizing the Levenberg-Marquardt algorithm (LMA) and hyperbolic tangent sigmoid function (HTSF) as the model hyperparameters, the study investigated several network structures and the simulations revealed that the 7-20-1 is the best model among the topologies observed in this study. The results showed an R value of 0.99868, MSE and MAPE of 0000749 and 5.036%, respectively. Additionally, the Akaike Information Criterion (AIC) was used as another layer of metric to measure the effectiveness of the model. The least was observed in the 7-20-1 network structure indicating that this is the best among the topologies observed in this study. Moreover, a sensitivity analysis (SA) through Garson's Algorithm (GA) was performed to determine the relative contribution (RC) of the input parameters (IP) including the distance from the ridge, enclosure classification, surface type, elevation above ground level, wind direction, basic wind speed, and presence of wall/surface opening to the effective net wind pressure. The findings presented that the basic wind speed is the most significant parameter to the effective net wind pressure value. The results of this study can be utilized in considering appropriate configuration to minimize the effects of wind pressure in structures.

There have been discussions about the gender gap in STEM majors. While some fields (e.g., Biomedical Sciences) have a high proportion of women workers, the Computer Science (CS) and Software Engineering (SE) disciplines are lacking female specialists. Universities worldwide are implementing different practices to attract more women to the CS and SE programs. This literature review aims to collect literature on this topic, identify the research tendencies, and collect female-inclusive practices. This paper presents the main findings from analyzing 143 selected papers from five academic databases (IEEE, ACM, Web of Science, Science Direct, and Scopus). The analysis revealed the need for inclusivity across all education stages, emphasizing practical studies beyond the classroom. Twenty-eight gender-inclusive practices were identified.

The Philippines is currently grappling with some of the hottest temperatures on record, presenting substantial public health challenges. Alarmingly, the temperature in Metro Manila reached a staggering 38.8 degrees Celsius (℃) this year, producing a perilous heat index (i.e., “feels like” temperature) of 45 ℃ due to high humidity (1). This recent spike in heat has surpassed the previous record set in 1915, and with Philippine Atmospheric, Geophysical and Astronomical Services Administration’s (PAGASA) reporting that heat indices could potentially soar above 52 ℃ in certain areas (2), the risk to public health is significant (3). According to the Department of Health (4), the Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA) classified temperatures ranging from 33–41 ℃ as “extreme caution” levels, while 42–51 ℃ falls into the “danger” category. Temperatures of 52 ℃ and above are classified as “extreme danger”, where heat stroke is imminent. This issue is not confined to the Philippines, as many Asian countries are experiencing unprecedented heatwaves (5), making this a broader regional crisis that demands urgent attention and action.

This letter highlights an escalating concern regarding predatory conference speaker invitations that are currently plaguing academia. Such invitations are frequently issued to individuals outside their areas of expertise, for instance, non-healthcare professionals being invited to health-related conferences. This issue poses a substantial threat as it compromises the integrity of legitimate academic discourse and carries the risk of propagating unchecked and potentially detrimental information. To avoid becoming ensnared by these predatory practices, it is paramount to undertake thorough due diligence. Consequently, this letter also outlines the characteristics of predatory speaker invitations. These guidelines underscore the necessity for the academic community to remain vigilant and judicious, thereby recognizing these overtures for what they truly represent: a deceptive temptation that leads away from authentic scholarly engagement toward a facade of recognition and prestige.

The evolution of AI technologies, exemplified by ChatGPT, signifies a transformative shift in our interactions with information and each other. This shift necessitates that our policies and mental health interventions adapt to protect the psychological well-being of individuals in this digital age. It is imperative that not only the psychiatric community but also policymakers, technologists, ethicists, and indeed, the broader society, engage actively in leading the development of comprehensive policies that confront these challenges directly. Encouraging collaboration across these diverse sectors is crucial. By fostering an environment of proactive policy development and implementation, we can collectively ensure that our engagement with AI not only supports but also enhances our mental health, rather than detracting from it. Together, we can harness the positive potential of AI, ensuring it serves as a tool for improving mental health outcomes and the overall quality of life for people around the globe.