Power system protection is essential for maintaining the reliability and stability of electrical grids, ensuring continuous service, and preventing catastrophic failures. As power systems evolve to incorporate renewable energy and increasingly complex configurations, the role of Artificial Intelligence (AI) in enhancing protection mechanisms has become indispensable. This paper reviews the integration of AI in power system protection, highlighting its potential to improve fault detection, adaptive protection strategies, predictive maintenance, and real-time monitoring. AI techniques, including machine learning, deep learning, and expert systems, offer significant advancements in overcoming the limitations of traditional protection schemes. Furthermore, the integration of AI contributes to the development of resilient and sustainable infrastructure, supports innovation in intelligent urban systems, and enhances the reliability of modern power grids. Despite its promising potential, challenges such as data scarcity, model scalability, and real-time processing need to be addressed for effective implementation. This review synthesizes the current literature on AI applications in power system protection, comparing them with conventional methods, and provides information on future research directions and practical applications to improve energy reliability, sustainable urban development, and industrial innovation.

With the increasing popularity of Raspberry Pi (RPi) comes the increase in available use cases and projects that utilize this single-board computer. One of these uses cases is the RPi Cluster, where multiple nodes are connected to a local network to form one logical resource pool. Implementing this build, however, can be a tedious and needs manual intervention such that each node needs to be configured to the cluster, one-by-one. While easily implementable if the scale is relatively small, the task becomes complex if several nodes need to be configured all at once. To save time, as well as to automate the whole process, developers and hobbyists would use Docker Swarm as an alternative. This paper explores the possibility of going beyond the popular Docker Swarm by proposing the use of OpenStack, a Cloud Computing Platform, as an alternative. Document analysis and review of existing research were used to prove the build’s technical feasibility. After comparing five relevant use cases, it can be concluded that deploying OpenStack to the RPi Cluster, or OpenStack-on-RPi, is feasible, though further research and testing that will optimize this use case is recommended.

Corrosion stands as the primary cause behind the diminished service life of reinforced concrete structures, particularly in environments such as ports, harbors, bridges, and other offshore and near-shore locations where chloride-induced corrosion poses a significant threat. This study investigates the efficacy of three key parameters polypropylene fiber ratio (FR), concrete cover (CC), and bar diameter (∅)—in minimizing corrosion (CL) in reinforced concrete structures. Central Composite Design (CCD) of Response Surface Methodology (RSM) is employed to determine the optimal conditions for these parameters. The number of samples for Impressed Current (IC) testing is determined through this methodology. Initial analysis utilizing the full quadratic model yields a Predicted value R2 of 60.77%. However, employing backward elimination enhances the predictive capability, resulting in an improved R2 value of 87.53%. Sensitivity analysis utilizing the coded units of the RSM model reveals that the polypropylene fiber ratio exerts the most significant impact on corrosion levels, with a sensitivity value of -4.932. Consequently, optimization efforts are focused on this parameter, leading to the identification of an optimized value of 1.17% for FR, which results in minimal corrosion. This research underscores the effectiveness of employing RSM techniques in optimizing corrosion mitigation strategies in reinforced concrete structures, with FR emerging as a critical determinant in achieving corrosion resistance.

Sustainable development on remote islands encounters hurdles such as environmental, logistical, and economic constraints. Small islands face insufficiency and limitations in construction materials due to limited natural resources. Lightweight structures emerge as transformative solutions to overcome challenges in construction. Santiago Island faces construction complications intensified by adverse weather and logistical issues, particularly disruptions in material transport. This study proposed a lightweight structure by addressing logistical challenges, environmental challenges, and structural costs, assessing existing materials including bamboo, exploring sustainable design principles, incorporating structural standards, and constructing and evaluating a typhoon-resistant structural system. The study results of the identification of lightweight construction materials and techniques available on Santiago Island and the investigation of a sustainable design system, which is the A-frame house design or triangular-shaped structure, foster sustainable development and resilience for remote islands. Structural analysis of the lightweight sustainable design was also identified through STAAD to comprehensively check all the structural members. The results of the research propose a hopeful remedy for crafting a cost-effective, environmentally friendly, and durable architectural blueprint tailored specifically for communities residing on small islands within low- to middle-income nations.

In this paper, the performance of feed-forward backpropagation (BP)—artificial neural network (ANN) was evaluated in predicting the construction project cost (CPC). The models include several factors involving the floor area (FA), number of floors (NF), structural material type of the building (MT), height of the building (HB), number of columns (NC), area of the concrete hollow blocks wall (CHB), volume of concrete (VC), weight of steel (WS), and contract duration (CD). The developed neural network model was evaluated based on several accuracy metrics such as correlation coefficient (R), mean squared error (MSE), mean absolute percentage error (MAPE), and Akaike Information Criterion (AIC). The simulation results showed that the governing model has an excellent R value of 0.99885 and a MAPE of 2.5826%. The comparison results between the ANN and multiple linear regression (MLR) suggest that the ANN model provided superior performance with MAPE which is 4.777 times better than that of the MLR model. Moreover, the lowest AIC value was observed in the 9–21-1 network structure suggesting that this is the governing network model for predicting the CPC. The sensitivity analysis (SA) using Garson’s Algorithm (GA) quantitatively determines the relative contribution (RC) of the input parameters (IP) to the construction project cost. The developed model could be utilized as a support instrument for minimizing the cost overruns and losses that may have been incurred in a construction project (CP).

The COVID-19 pandemic disrupted work systems, family, and social life. The mandatory lockdown forced employees to shift to work-from-home (WFH) setup which exposes them to WFH conflicts. This study provides a machine learning—based approach for prioritization of factors affecting WFH conflicts during the COVID-19 pandemic. These factors include time spent with the family (F1), leisure activities (F2), household task (F3), family quality of life (F4), agitation and anger from work (F5), financial obligations (F6), family presence (F7), family issues (F8), health-related (F9), and work burn-out (F10). Using the backpropagation (BP)-artificial neural network (ANN) modeling and Olden’s connection weights (CW) approach, the order of influence of these parameters to the productivity rating (PR) was observed. Based on the results, the 10-21-1 network structure is the best performing model (BPM) with correlation coefficient (R) = 0.98173 and mean squared error (MSE) of 0.02607. This network topology also provided the least Akaike Information Criterion (AIC) value showing that it is the best model. Using its connection weights (CW) through Olden’s approach, the results showed that the financial obligations are the most influential parameter (MIP) while the household task is the least influential parameter to the productivity model. The utilization of machine learning techniques proved to be effective in determining the influence of predictors on the target output. The obtained findings from the study could assist the organization and managers in resolving work-from-home conflict and productivity issues.

Transportation is critical, especially in rural areas as it provides the mobility to people to access different activities satisfying their daily needs. The purpose of this research is to create an artificial neural network (ANN) trip generation model (TGM). 500 households (HH) were surveyed to obtain the independent variables used in the modeling process including the HH size (HHS), number of children in the HH below 7 years old (NCHHBS), number of HH member from 7 to 59 years old (NHHMSF), number of HH member above 59 years old (NHHAF), number of working member of the HH (NWMHH), number of school children in the HH (NSCHH), number of helpers in the HH (NHHH), number of motorize vehicles in the HH (NMVHH), HH income (HHI), highest educational attainment (HEA), head of HH age (HHHA), and number of driver’s license holder in the HH (NDLHHH). Using the Levenberg–Marquardt algorithm (LMA) as the training algorithm (TA) and hyperbolic tangent sigmoid (HTS) function as the activation function (AF), the governing TGM was observed in the 12–25-1 network structure with the highest R value = 0.98476 and least Mean Absolute Percentage Error (MAPE) of 9.04%. Moreover, the governing network structure achieved the minimized Akaike Information Criterion (AIC) value at 25 hidden neurons (HN) indicating that the network has already been generalized and was the best model among those observed in this study. The outcomes of the research showed the efficacy of artificial neural networks in developing trip generation prediction models (PM).

crucial component of growth in infrastructure is estimating construction costs (CC) for pipelaying projects (PP) related to water distribution networks, which guarantees the effective and long-term provision of safe drinking water to communities. In this paper, an artificial neural network (ANN) and multiple linear regression (MLR) model was developed for predicting construction cost for pipelaying projects. The governing model (GM) has a model structure of 9-20-1 (input-hidden-output) with an R = 0.99992. The findings revealed that the ANN-based network was 13.127 times better than the MLR model, based on its MAPE of 3.214 and 42.194%, for ANN and MLR, respectively. The best network also has the lowest Akaike Information Criterion (AIC) among the simulated network structures indicating that it is the best network. The relative importance (RI) of the independent variables including the length, diameter, material type, hydrotesting works, disinfection works, demolition works, restoration works, duration delay, and liquidated damages were calculated utilizing the Garson’s algorithm (GA). It was seen using GA to compute the relative importance of each parameter that the order of influence is seen as restoration works (RW) > length > demolition works (DeW) > material type (MT) > diameter > disinfection works (DiW) > hydrotesting works (HW) > duration delay (D%) > liquidated damages (LD) wherein the restoration works is the most influential parameter. The findings of the study could be used as a reference for better planning and managing pipelaying project activities.

The research was derived from extensive literature reading and addressed the gap in strengthening existing buildings. The study aims to create a model that would correlate the concrete's compressive strength to nondestructive tests (NDTs), establish the strength of in-situ structural members of an existing building using the model, and propose retrofitting intervention strategies as mitigation measures against ground motions. The study presents the artificial neural network (ANN) as the governing model for strength predictions over multi-linear and quadratic regressions. Sensitivity analysis gives prevalent insights into which factor influences the forecast among the input variables. This prediction model has been initiated to evaluate the in-situ strength of the case study building for the analysis following nonlinear static procedures. Two retrofitting interventions were then developed to compare with the performance of the existing three-story building. Predominantly, a performance-based design employing pushover analysis was done where the idealized curves were generated, projecting the base shear and displacements concerning the behavior of the building (ductile or inelastic behavior). This research evaluates the passing criteria of the building based on the performance objectives provided by American Society of Civil Engineers (ASCE) 41–17. The structural member checks in terms of member chord rotations, member shear forces, joint shear stress, and inter-story drifts in connection with the base shear and target displacements evaluation proposed the best retrofitting intervention. The research showed that Case II (retrofitting by shear walls) intervention provided the lowest base shear and passed the considered member checks than RC jacketed with FRP wrapping interventions.

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.