The integration of 3D printing technology into water infrastructure offers innovative solutions to the pressing challenges faced during disaster relief and response. This review paper explores the advancements in 3D printing technology and its applications in enhancing water infrastructure, especially during disaster relief operations. It compiles existing literature to highlight the current state of knowledge, focusing on the potential benefits, challenges, and future potential of 3 D printing for creating essential water infrastructure components. The study emphasizes the technology's capability to produce customized, on-demand solutions that are both cost-effective and efficient. By addressing the critical aspects of 3D printing applications in disaster scenarios, this paper aims to provide a comprehensive understanding of how this technology can revolutionize clean water accessibility and improve quality during emergencies.

In this study, we use various RNN architectures namely, RNN, Bi-LSTM, and GRU — alongside BERT to analyze sentiment across university departments. Our aim is a comparative analysis of these models in sentiment classification within education. We collected and pre-processed textual data from multiple departments for balanced training and validation. Results showed that traditional RNNs achieved 90% accuracy, Bi-LSTM 93%, and GRU 89%. BERT, leveraging its Transformer architecture, outperformed with 94% accuracy. These findings highlight the superiority of BERT in capturing complex language patterns for sentiment analysis. This study underscores the potential of advanced neural network architectures to gain insights into departmental sentiments, informing policy decisions and educational strategies. Aligning with sustainable development goals in education, we aim to use AI models to develop effective, inclusive, and responsive educational strategies, enhancing quality and accessibility.

The factor evaluation for sustainability implementation in post-disaster reconstruction projects was conducted using artificial neural network (ANN)-based sensitivity analysis. The current study involves the utilization of Levenberg-Marquardt algorithm (LMA) for the ANN model development. The findings demonstrated that the governing network structure is 10-3-1 utilizing input parameters (IP) including waste management (Sl), environmental impact management (S2), support of management (S3), construction cost (S4), public health and safety (S5), user security (S6), noise pollution (S7), energy consumption (S8), public services (S9), and recycling (S10). The results revealed that the governing model (GM) has a performance of correlation (R) = 0.99813 and Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) of 0.00110 and 0.4240/0, respectively. Moreover, a sensitivity analysis (SA) using Garson's algorithm (GA) reveals a trend of the relative value (RV) was observed to be construction cost > public services > support of management > user security > environmental impact management> noise pollution> public health and safety> energy consumption > recycling > waste management wherein the construction cost is the most influential parameter to the sustainability rating in post-disaster reconstruction projects (PDRP). The study shows how well ANN can detect the crucial elements that determine a project's sustainability rating in the post-disaster reconstruction projects.

The fast progression of Artificial Intelligence (AI) technology has elicited substantial ethical issues, especially within engineering fields that directly impact society. This study seeks to establish a framework for integrating Ethical AI ideas into engineering curriculum, therefore preparing future engineers to address the moral, social, and legal ramifications of AI. The framework incorporates Ethical AI principles into current course formats, encompassing introductory, enabling, and demonstrative courses, with particular focus on subjects like Science, Technology, and Society, Professional Engineering Ethics, and thesis/capstone projects. The paper recommends a curriculum update that complies with industry norms and equips students to embrace responsible AI practices, based on a thorough analysis of pertinent Commission on Higher Education (CHED) Memorandum Orders (CMOs) and literature. The research also presents evaluation rubrics to gauge students' comprehension and implementation of Ethical AI concepts in their academic projects. The paper suggests that integrating Ethical AI into engineering education enables universities to cultivate engineers who possess both technical proficiency and a robust ethical framework about AI technology.

The fusion of molecular-scale engineering in nanotechnology with machine learning (ML) analytics is reshaping the field of precision medicine. Nanoparticles enable ultrasensitive diagnostics, targeted drug and gene delivery, and high-resolution imaging, whereas ML models mine vast multimodal datasets to optimize nanoparticle design, enhance predictive accuracy, and personalize treatment in real-time. Recent breakthroughs include ML-guided formulations of lipid, polymeric, and inorganic carriers that cross biological barriers; AI-enhanced nanosensors that flag early disease from breath, sweat, or blood; and nanotheranostic agents that simultaneously track and treat tumors. Comparative insights into Retrieval-Augmented Generation and supervised learning pipelines reveal distinct advantages for nanodevice engineering across diverse data environments. An expanded focus on explainable AI tools, such as SHAP, LIME, Grad-CAM, and Integrated Gradients, highlights their role in enhancing transparency, trust, and interpretability in nano-enabled clinical decisions. A structured narrative review method was applied, and key ML model performances were synthesized to strengthen analytical clarity. Emerging biodegradable nanomaterials, autonomous micro-nanorobots, and hybrid lab-on-chip systems promise faster point-of-care decisions but raise pressing questions about data integrity, interpretability, scalability, regulation, ethics, and equitable access. Addressing these hurdles will require robust data standards, privacy safeguards, interdisciplinary R&D networks, and flexible approval pathways to translate bench advances into bedside benefits for patients. This review synthesizes the current landscape, critical challenges, and future directions at the intersection of nanotechnology and ML in precision medicine.

In recent years, the construction sector in the Philippines has faced significant challenges stemming from various events and occurrences, leading to cost overruns and delays in project timelines. A critical element for every construction undertaking's accomplishment is cost evaluation. Precisely approximating the cost of a project involves thorough consideration of various elements, making it a difficult undertaking to forecast. Several building constructions nowadays produce high cost overrun because of unforeseen change in the project budget that raises the overall project cost such as the complexity of the building system and the organization’s environment. The aim of this paper is to offer a potential prediction for cost estimation, with the goal of minimizing the substantial risk of cost overruns in low- to mid-rise buildings. In this study, the structural elements for low- to mid-rise buildings were utilized from building constructions, such as the number of exterior walls (QEW), type of construction material (TCM), building height (HB), total gross area (TGA), building footprint area (BFA), type of occupancy (TO), number of floors (NF), quantity of shear walls (QSW), and number of columns (NC); an artificial neural network (ANN) model was employed in this research to establish a model for forecasting the total construction cost (TCC). With a correlation value (R) of 0.999890 and a mean absolute percentage error (MAPE) of 0.601%, the modeling results shown that the best model structure was 9-25-1 (input-hidden-output), indicating its effectiveness and efficacy in forecasting the TCC. The impact of each variable employed as an input variable (IV) in the model establishment was seen employing the connection weights (CW) through Garson’s algorithm (GA). The calculation exhibited the order of influence observed as QSW > NC > HB > NF > QEW > TGA > BFA > TO > TCM, wherein the quantity of the shear walls is seen to have the most contribution to the construction cost. Moreover, to check its performance versus other prediction modeling tools, a multiple linear regression (MLR) model was also created and compared to the governing prediction model (GPM). The MAPE of the BP-NN is 7.108 times better than that of the created MLR model.

Delays in bridge construction are crucial problems that slow down the economic development in an area. In this study, an artificial neural network (ANN) model was utilized to create a model for predicting the duration delay in bridge construction projects which includes the project amount, length of bank protection, length of bridge approach slope protection, total area of bridge approach, number of item of works, number of foundations, type of foundation, number of girders, type of girder, number of lanes, number of spans, total width, total length, and type of construction as the independent variables (IV). The modeling results showed that the best performing model is the 14–14–1 network with R = 0.99406 and MAPE of 3.524%. By removing each of the parameters, the influence of the independent variables to the duration delay was determined. Using the sensitivity index method, the findings revealed that the ranking of influence of the factors (IF) to the duration delay was observed as LBASP > NS > TW > TC > NIW > LBP > PA > TABA > TL > NG > NF > NL > TG > TF with the length of bridge approach slope protection was seen to be the most influential parameter (MIP) to the duration delay.

This study focuses on the evaluation of a faculty Performance Modeling System, recognizing the critical role of faculty performance in educational quality and institutional success. Although previous research has often concentrated on the technical development and predictive capabilities of such systems, this paper shifts the focus to user acceptance and system efficacy from the perspective end-users, the faculty. To achieve this, the researchers propose and apply a Modified Technology Acceptance Model (TAM) as the theoretical framework for evaluation. This modified TAM incorporates specific constructs relevant to the academic environment and faculty roles, such as perceived impact on teaching effectiveness and perceived relevance to professional development, alongside traditional TAM constructs like perceived usefulness and perceived ease of use. The evaluation methodology involves assessing faculty perceptions and attitudes towards the system, utilizing both quantitative and qualitative data to understand factors influencing its adoption and continued use. The findings are expected to provide valuable insights into the practical applicability and user acceptance of faculty modeling systems, guiding future design and implementation efforts to ensure these tools effectively support faculty growth and institutional objectives.

Addressing the worldwide crisis of inadequate physical education (PE) programs requires immediate attention. Despite the advocacy of international organizations like UNESCO and WHO, there still needs to be a significant gap in understanding the effectiveness of PE initiatives globally. Cultural, socio-economic, and policy differences further complicate evaluating and improving these programs. More comprehensive research is needed to promote academic achievement, well-being, and overall health. This is where Global Innovations in Physical Education and Health comes in, a groundbreaking solution poised to revolutionize PE on a global scale. This innovative book serves as a beacon of hope by exploring diverse teaching strategies and creative methods worldwide. Bridging critical research gaps empowers policymakers, educators, researchers, administrators, and health professionals with actionable insights to enhance the quality and inclusivity of PE programs. With its comprehensive coverage of topics such as adaptive PE, nutritional education, and global health initiatives, this book provides a roadmap for transforming PE into a catalyst for holistic health and lifelong well-being.

PE often finds itself overlooked in the broader discourse on educational innovation, despite its crucial role in fostering lifelong health and well-being. Unlike subjects that easily attract attention due to their academic prestige or technological allure, PE is sometimes seen as secondary, relegated to the sidelines of educational reform. Yet, in an age where global health challenges such as obesity, mental health disorders, and sedentary lifestyles are increasingly prevalent (World Health Organization, 2020), the significance of PE cannot be understated. The Global Innovations in Physical Education and Health book seeks to challenge this perception by highlighting the transformative potential of innovative approaches in PE and health education (Garcia, Lopez Cabrera, et al., 2023). It brings together 69 experts from nine countries—Indonesia, Philippines, China, Ireland, United Arab Emirates, Portugal, India, USA, and Canada—to explore how these innovations are reshaping the way we teach, experience, and benefit from PE, making it a field deserving of as much attention and innovation as any other educational domain. This book serves as a crucial resource in guiding the development of more effective, inclusive, and culturally aware health and PE programs. It sets the foundation for actionable teaching and learning models in various educational and cultural contexts. The coverage includes insights into how different countries approach PE, the integration of health education within these programs, and their impact on student health and academic achievement.