The construction sector is a substantial generator of waste and carbon dioxide emissions worldwide. The use of sustainable materials in construction could minimize its negative effects on the environment. This research is intended to offer a soft computing model for predicting the deformation and parallel bending strength (PBS) of Guadua angustifolia applying an artificial neural network (ANN)-particle swarm optimization (PSO) approach and employing the data obtained from the experimental tests performed in the study. The input parameters (IP) utilized in the modeling process include the outside diameter, wall thickness, minimum length, external taper, perpendicular distance of bow, ISO ovality, eccentricity, actual shear span, area, modulus of elasticity, density, and linear mass. The resulting models showed R values of 0.99076 and 0.99976 and MAPE of 0.936% and 0.345% for deformation and PBS, respectively. The findings of the sensitivity analysis (SA) also exhibited that ISO ovality and eccentricity were the most important parameters to the deformation and PBS models. Research outcomes demonstrated the effectiveness of the ANN-PSO approach for predicting the deformation and parallel bending strength characteristics of Guadua angustifolia. The modeling approach proposed in this study could be utilized for speeding up the material characterization phase of similar construction materials.

As part of economic progress, there has been a surge in construction projects in the past few years. It is known that construction has negative and detrimental effects on the environment. The use of sustainable practices needs to be integrated into the construction processes to minimize these negative impacts. This paper introduces a prioritization method for determining the most influential factor to the implementation of environmental smart guidelines for sustaining environmentally friendly programs using sensitivity index (SI) method. Several areas were considered in this study including environmental, ecological, social, and economic impacts. Using the backpropagation-neural network (BPNN) modeling, four models for environmental, ecological, social, and economic impact ratings were developed with 15-31-1, 4-9-1, 9-19-1, and 2-5-1 network topologies (input neuron-hidden neuron-output neuron) for environmental, ecological, social, and economic impact rating, respectively. The R values for the models were observed to range from 0.97652 to 0.99901. To determine the trend of the impact of the subsets of each areas, sensitivity index method was used and the findings revealed that the water pollution reduction in the project is the most influential subset to the environmental impact rating, presence of planting area in the project for the ecological impact rating, fair sharing of benefits of the project for the social impact rating, and self-liquidation capacity of the project for the economic impact rating. The results of the study could assist managers and planners in addressing key areas and concerns in the effective implementation of smart and sustainable practices in projects.

The world is rapidly changing and experiencing a rapid increase in population, especially in cities and urban areas. The growth in population in these urban areas results in a need for a more competitive and sustainable system. In the onset of the fourth industrial revolution, the trend in equipping these cities with advanced mechanisms in improving the quality of life and service in these cities is needed. In this study, a neural network - based approach for factor prioritization was implemented to determine the most influential factor in the smart city (SC) development implementation in the Philippines. Using the neural network internal characteristics including the Levenberg-Marquardt (LM) as the training algorithm (TA) and the hyperbolic tangent sigmoid (HTS) as the transfer function (TF). The study utilized the 18-37-1 network structure for the neural network model with an R value of 0.95003 and MSE of 0.032609. The connection weights (CW) from this network were utilized to calculate the relative importance (RI) of the factors affecting the smart city implementation through Garson's Algorithm (GA). The results of the study revealed that the most influential parameter (MIP) to the smart city implementation is the SCPD2 - analyzing solutions fit with strategic objectives. Moreover, the results and findings of the study could assist the city planners and SC strategy development authorities in the integration of different systems in the SC implementation.

The rise of digital platforms has led to a massive influx of textual data. While traditional textual analysis techniques have been effective, analyzing large datasets is becoming impractical due to the required time and resources. To demonstrate the usefulness of text mining as an alternative, this study analyzed data extracted from an emergency remote learning (ERL) environment. Free-form responses from a series of cross-sectional surveys (2020–2022) were analyzed using word frequency, collocation, concordance, topic modeling, and sentiment analyses. According to the findings, the most commonly occurring unigram and bigram in the text corpus were “hard” and “mental health,” respectively. Three primary themes based on lived experiences were identified, namely individual, academic, and technological challenges, and another three themes emerged from coping strategies, including entertainment, relationship, and health-related mechanisms. Negative sentiment toward the ERL setup was also evident in the text corpus. Overall, the combination of text mining techniques allowed for a comprehensive exploration of the linguistic features of the corpus and provided a multifaceted understanding of the selected phenomenon. Consequently, this study endorses text mining as a methodology for analyzing large volumes of textual data.

Loan management in transportation cooperatives involves the assessment, approval, disbursal, and monitoring of loans for various member needs, ensuring compliance with regulations, and providing support throughout the loan lifecycle. The traditional manual processes and paperwork used in loan management are prone to errors and data redundancy, are time-consuming and inefficient, lack transparency, and cause difficulty in data analysis and reporting, highlighting the need for a proficient Loan Management Website and Mobile Application. This study presents the development of a comprehensive system that efficiently handles data, integrates the credit scoring system, and leverages the FICO score for accurate loan amount recommendations, enhancing decision-making and enabling improved money monitoring, risk management, and record storage. The system's quality was evaluated using ISO 9126's software characteristics such as functionality, usability, reliability, and portability, yielding an overall mean score of 4.71 for the web application and 4.68 for the mobile application, indicating its seamless facilitation of loan transactions. The integration of additional technologies like Decision Support and SMS notifications enhances risk management and financial transparency, fostering increased capital generation for cooperatives, thereby providing opportunities for business growth and sustainability. The developed Loan Management System offers a streamlined approach compared to traditional methods, facilitating faster loan application, approval, and payment processes. Overall, this research contributes to the advancement of loan management practices in transportation cooperatives by leveraging technology, enhancing decision-making processes, and promoting financial efficiency and transparency.

The adoption of information technology in healthcare has resulted in novel solutions for improving patient care and operational efficiency. This study details the design and implementation of a web-based clinic management system augmented with sentiment analysis to evaluate and enhance patient satisfaction. The system utilizes natural language processing and machine learning to autonomously assess patient feedback, understanding feelings (negative, positive, and neutral) regarding critical aspects such as waiting times, doctor-patient interactions, care efficacy, and overall clinic experience. The system underwent alpha and beta testing, commencing with controlled trials and then involving real-world evaluations with clinic attendants, physicians, and patients. An evaluation conducted in a dermatology clinic revealed the system's effectiveness in detecting service deficiencies and informing enhancements. Thus, this study suggests that the integration of sentiment analysis in clinical management systems enhances data-driven decision-making, hence improving patient experiences and optimizing operations.

This chapter explores the developmental implications of artificial intelligence (AI) in contemporary educational settings. Employing a dual-methodological approach that combines interdisciplinary expertise with an integrative literature review, the chapter explores how AI technologies are reshaping student identity, emotional regulation, motivation, autonomy, and interpersonal relationships. Drawing on developmental psychology, educational theory, and empirical research, it interrogates the unintended consequences of algorithmic surveillance, pedagogical automation, and AI-mediated social interactions. The analysis highlights both the affordances and risks of AI in education, including its impact on emotional resilience, self-directed learning, and the construction of academic identity. Reframing the discourse away from purely technical efficiency toward developmental integrity makes visible the deeper human stakes of AI integration in education. The chapter consequently calls for ethical, inclusive, and human-centered approaches to AI design and implementation in schools.

Purpose: This study aimed to examine how physical education (PE) students engage with fitness content on TikTok. Methods: The evaluation involved 597 students enrolled in a PE 1 course across three campuses of a prominent university. Results: Findings show that students primarily watch TikTok videos for entertainment, with male students also seeking motivation and social interaction, while female students look for escape, advice, and guidance. Engagement is highest for videos featuring body transformations, fitness tips, and motivational content, with a tendency to apply learned exercises, tips, and nutrition education. Body transformations and motivational videos effectively arouse emotions and elicit affective responses. Conclusion: This research highlights diverse motivations and impacts of fitness content on TikTok among PE students, contributing to the literature on social media usage and offering insights for enhancing instructional practices in PE and understanding digital media interaction.

Biochar also known as ‘biocarbon’ or ‘biocoal’ is a material that has a charcoal similar property. It is obtained from thermolysis (pyrolysis) of biomass feedstocks and plant matters. It can help the process of eliminating carbon dioxide from the atmosphere. The biochar was considered as waste by industrial plants and considered no additional cost except for the transportation. Biochar was tested for its chemical properties in Department of Science and Technology as a parameter for Simapro. Environmental and health impact were analyzed in this study for concrete with biochar as partial replacement for cement. Different mixtures with zero percent to twenty percent biochar replacement was simulated using life cycle assessment with the help of Simapro. Different sources in Luzon island, Philippines were gathered and found out that sources in southern part of Luzon is the best sources for biochar because of its near location that decreases the effect of transportation. Also, concrete with biochar replacement with or without considereing the effect of transportation yields greater health and environmental impact compared to mixture without biochar replacement.

The integration of digital technologies in education has profoundly transformed teacher education, necessitating a focus on creativity, problem-solving, and inquiry-based learning. Despite the expanding literature on technological self-efficacy, creativity, and curiosity in education, significant gaps persist in understanding their relationships, especially in teacher education. Utilizing a cross-sectional design, the study applies PLS-SEM to investigate the relationships among technological attitudes, technological self-efficacy, technological problem-solving engagement, intrinsic motivation, learning engagement, pedagogical knowledge, content knowledge, creative reasoning, and curiosity among 875 respondents from a state university in Cebu City, Philippines. The findings reveal that positive technological attitudes significantly enhance technological self-efficacy, which, while influencing technological problem-solving engagement, does not directly impact creative reasoning or curiosity. Additionally, both technological problem-solving engagement and intrinsic motivational factors substantially contribute to fostering creativity and curiosity. The strong roles of pedagogical knowledge and content knowledge further emphasize the need for teacher education programs to incorporate holistic strategies that combine technological engagement with pedagogical frameworks. These insights underscore the importance of equipping pre-service teachers with the skills and knowledge necessary to cultivate creativity and curiosity in their future classrooms, thereby enhancing overall educational effectiveness.