Part of speech tagging (POS) is crucial in natural language processing, identifying the grammatical categories of words in sentences. This research highlights the lack of focus on POS tagging for Asian languages, particularly Cebuano. Inadequate research on Cebuano religious text has hindered linguistic documentation and understanding its grammar and vocabulary. This study introduces a Parts-of-Speech Tagging for Cebuano utilizing a Modified Viterbi Algorithm. The researchers also apply a method for handling unfamiliar words. Results indicate that the algorithm performs exceptionally well on a religious text corpus comprising 50,000 datasets, achieving an accuracy of93%,precision of90%, recall of 90. 52%, and an F1-score of92%. These results highlight the algorithm's effectiveness in tackling language challenges within specific genres. Furthermore, the research supports the Sustainable Development Goals (SDGs) by promoting linguistic diversity and advancing inclusive language technologies. The study also provides valuable insights into Cebuano's linguistic characteristics and grammatical structures, laying a solid foundation for future research in natural language processing.

Cavite is a highly urbanized province situated near Metro Manila and has the highest population growth rate in the country. Water resource management and water-related risk mitigation is one of the major challenges the province faces. Cañas River Watershed is one of the major river systems in the province which covers major cities and municipalities. Effective streamflow monitoring in this watershed has not been achieved due to the inadequacy of monitoring stations around the province. This study aimed to develop an LSTM model to predict the streamflow in Cañas River Watershed at the Panaysanayan river gauge using the available weather parameters in two weather stations in the province, namely: Sangley Point Synoptic Station and Cavite State University (CvSU) Agrometeorological Station. Using the short-term data dated from 2014 to 2019 obtained from the stations and the river gage, the Long Short-Term Memory (LSTM) model successfully predicted the streamflow. Based on the model performance evaluation the values of Nash-Sutcliffe Efficiency (NSE) for the training and test were 0.90-0.91 and 0.87-0.89, respectively which indicates a high predictive accuracy. On the other hand, the Percent Bias (PBIAS) results in training and testing ranges 0.60% -8.04% and 1.92% -8.32%, respectively, which indicates a low bias prediction. The model tends to underestimate values, especially high magnitude flows. The RMSE-to-Standard Deviation Ration (RSR) results in training and testing ranges from 0.30-0.31 and 0.34-0.35, respectively, which indicates a good predictive power. The model results also show a good performance in developing a flow duration curve in the river to determine its dependable flow. The R2-value between the observed and predicted flow at different probability of exceedance is 0.9938. The dependable flow of Cañas River Watershed at Panaysanayan river gauge was 60 liters per second based on the observed flows and 61.12 liters per second based on the predicted flows.

3D printing, or additive manufacturing, is transforming electrical engineering by driving advancements in sustainable energy systems, urban infrastructure, and industrial innovation. This paper explores its applications in fabricating energy-efficient components, such as photovoltaics, wind turbine parts, and energy storage systems, as well as its role in advanced prototyping and smart grid technologies. The adoption of advanced materials, including conductive polymers and biodegradable composites, supports the development of renewable energy systems and customized solutions for urban and industrial applications. By reducing material waste, lowering production costs, and accelerating innovation cycles, 3D printing fosters sustainable manufacturing practices and resilient infrastructure development. Challenges such as material compatibility, scalability, and costs are discussed, alongside emerging technologies like artificial intelligence (AI) and the Internet of Things (IoT), which enhance optimization and broaden applications. This study highlights the critical role of 3D printing in advancing sustainable energy, urban development, and industrial modernization.

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.