Marine ecosystems face unprecedented threats from pollution, overfishing, and climate change, creating an urgent need for conservation initiatives. While consumer awareness of ocean degradation is increasing, there remains a persistent gap between environmental concern and actual purchasing behavior toward sustainable products. This study aims to examine public readiness to adopt OceanGuardian, a sustainable e-commerce platform for marine conservation products, by integrating behavioral, technological, and environmental perspectives. Using a modified Extended Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework, survey data from 600 respondents were analyzed with machine learning models, including Support Vector Machines, Random Forests, and XGBoost, to identify key determinants of consumer intention and use behavior. Results indicate that social influence, performance expectancy, affordability, and habit formation significantly predict adoption, with Support Vector Machines achieving the highest predictive accuracy (92.5%). The findings highlight the potential of artificial intelligence to enhance consumer behavior analysis while recognizing challenges such as economic barriers and consumer skepticism. The study offers theoretical contributions by extending UTAUT2 with environmental factors and provides practical insights for policymakers and businesses to design strategies that foster sustainable shopping and strengthen marine conservation efforts.

Social class inequality is the stratification of classes based on wealth, income, influence, and access to resources. Individuals who are categorized as low class face unequal access to basic needs, inaccessible health services, deterioration of mental health, entry into the poverty cycle, and crime. As suggested by the relative references, the factors of social class in the Philippines are underemployment, unemployment, and income inequality. In alignment with the Sustainable Development Goal (SDG) 10 entitled Reduced Inequalities, the proponents' objective is to raise awareness towards social class inequalities in the Philippines by creating a 3D first-person action investigation game entitled Overdrive to showcase a premise based on the experiences of the lower class and its possible solutions. The development used the SCRUM development cycle methodology. The game is accompanied by a Content Management System-based website to promote the game materials. To gather the data, the proponents conducted beta testing among IT students of the FEU Institute of Technology, and a few external technical and non-technical individuals. The testing was followed by a Likert scale questionnaire which gathered the satisfaction level of the game and assets, integration of the study within the story, and the website. A weighted average mean was utilized to evaluate the data. The overall data resulted in a mean of 4.35 which interprets a ‘Satisfied’ rating towards the created game and conducted project.

Blood banking is the process of collecting, separating and warehousing blood. There are numerous file-based repositories of blood bank management that exist for storing data for blood bank ecosystem such as hospitals and centers. This functions for maintaining the information of donors, availability of blood, and transaction information. Currently, these systems are effort intensive, costly, and failed to achieve efficiency in terms of its filtering mechanism which makes repository penetrating faster and reliable. This paper introduces a new design for blood banking ecosystem with proper filtering solution using frame-based approach. The system has three major features: (1) blood camp setup module, (2) stocks management module which includes the blood donation and blood releasing, and (3) the filtering system module which shows the nearest blood camp with the available blood type based on the patients’ needs. Also, with the use of frame-based approach as filtering method, the system is more efficient and reliable compared to other blood banking repository systems. The system’s functionality was tested for its efficiency, usability, and reliability and the results are revealed in the survey. Conclusions and future work were also provided in this paper.

The blockchain phenomena is no longer about Bitcoin or cryptocurrency, it is beyond a common protocol to make it nearly impossible to create fraudulent transaction. Blockchain based architecture overall performance is subjected to storage expenses with high computational cost. This paper designed a new consensus protocol for Blockchain using Content Addressable Pattern with the adaptation of modified SHA256 algorithm. Although government, business and other entities interest of adapting blockchain to their processes, the complexity issues and operational cost is still a challenge to date. With the newly design consensus protocol the process of validating the transaction that involves tedious mining or solving cryptographic puzzles has been eliminated and move towards using signature to authenticate the transaction. Concatenation of all these elements is a generated hash value using modified SHA256. Since the hash is secured, the transaction is secured. Thus, the implementation of off-chain chanel instead of global consensus addresses the complexity and high computational cost of blockchain technology.

Data analysis is an integral part of research. Most researchers examine their results by using graphs, tables, charts, and figures. These methods are effective, but knowledge transfer is limited because it only depends on what the authors or researchers have presented. The need to scrutinise further the given data is essential. One way of addressing this problem is to utilise a graphical user interface (GUI), wherein a user can manually choose some parameters of an extensive dataset to display and analyse. In this paper, the results of the four variants of clustering techniques, namely the Ant Colony Optimization (ACO), Gaussian Mixture Model (GMM), K-Power Means (KPM), and Kernel-Power Density-Based Estimation (KPD), in grouping the wireless multipath propagations, are evaluated through the use of a GUI. The accuracy performance of each clustering algorithm can be obtained by choosing in the GUI the corresponding channel scenario that the user would like to investigate. A deeper analysis of the clustering characteristics can also be done by selecting other parameters in the GUI. This selection gives a better understanding of the behaviour of each clustering technique and provides an effective way of presenting and analysing the different sets of data.