The fast proliferation of fake news on social media has sparked worry about its impact on public perception and decision-making. This study uses Social Network Analysis (SNA) to investigate the link between social media use, fake news perception, and diffusion. Using data from 310 active social media users, the study investigates important aspects such as time spent online, trust in social media content, and network centrality. The data show that spending time on social media has little effect on fake news exposure, however increased trust in online platforms considerably promotes misinformation dissemination. Furthermore, fake news propagation is not confined to extremely influential people; it occurs at all levels of network centrality. The study also shows that user attributes help to forecast the spread of fake news, but external impacts like cognitive biases and algorithmic considerations must also be considered. Predictive modeling with logistic regression and decision trees identifies crucial behavioral patterns in misinformation sharing. The decision tree study shows confidence in social media, network centrality, and time spent online as the key causes of fake news spread. The ROC curve study indicates that, while user attributes have some predictive potential, misinformation diffusion is influenced by a variety of external factors. These findings highlight the critical need for media literacy initiatives, stronger fact-checking tools, and better platform policies to combat disinformation. This study adds to the increasing body of knowledge about false news dynamics and offers practical techniques for mitigating its societal impact.

Multiple sequence alignment (MSA) is a fundamental technique in computational biology that compares protein, DNA, or RNA sequences to identify regions of similarity reflecting functional, structural, or evolutionary relationships. This systematic literature review examines the diverse land-scape of multiple sequence alignment algorithms, categorizing them based on their underlying approaches and analyzing their strengths, limitations, and applications. We explore seven major categories of alignment methods: dynamic programming, progressive alignment, iterative refinement, Hidden Markov Model-based, consistency-based, structure-based, and machine learning-based approaches. Through comprehensive analysis of recent benchmarks and literature, we identify key innovations, performance characteristics, and emerging trends in the field. This review provides a detailed overview of the evolution of multiple sequence alignment algorithms and their applications in modern bioinformatics.

Fiber-reinforced rigid pavement offers a viable option for sustainable concrete development due to its superior mechanical properties, notably enhanced durability and crack resistance. Nonetheless, cracks remain inevitable. Recent studies have concentrated on harnessing biological mechanisms to introduce self-healing properties to concrete. A particularly promising method employs microorganisms like Bacillus subtilis, which can induce the precipitation of calcium carbonate (CaCO3) when exposed to moisture and air. This microbial-induced calcite precipitation (MICP) efficiently seals cracks, thereby reducing need for manual repairs and extensive maintenance. This study examined the application of 2.36 × 109 CFU of Bacillus Subtilis ATCC 6633 as a bio-admixture in fiber-reinforced concrete pavement. The study assessed the effectiveness of various concentrations. The research concluded that Bacillus subtilis effectively stimulated self-healing of cracks in concrete width of 1–2 mm and material recovery over the seven (7) day period, as verified by Absorption Test (Sorptivity), Ultrasonic Pulse Velocity (UPV), and X-ray Diffraction Analysis (XRD). However, the efficacy of the self-healing process varied based on the concentration of Bacillus subtilis. Higher concentrations (10%) of Bacillus subtilis improve fracture healing but diminished overall material performance. Furthermore, the direct application of 5% Bacillus subtilis proved to be a highly effective variant among the tested formulations, exhibiting an enhancement of flexural strength by 13.13% at 14 days, surpassing the design specifications.

The construction of mid-rise residential buildings in Metro Manila faces constant challenges related to project control inefficiencies, leading to delays, budget overruns, and quality concerns. This study aims to rank the factors critical to project control mechanisms (PCM), providing insights into the key drivers of project success in mid-rise residential construction projects. An Artificial Neural Network (ANN) model was developed to validate these rankings, utilizing the LevenbergMarquardt training algorithm and tansig activation function. The model achieved exceptional predictive accuracy, with an overall R of 0.99445, along with a low MSE (0.007515) and MAPE (1.6808%). Using the connection weights from the model, the analysis revealed that stakeholders influence, technology integration, and contractor performance are the top three most critical factors, highlighting the importance of collaborative decision-making, digital transformation, and contractor accountability. Resource allocation, quality standards, and schedule delays ranked mid-tier, while budget management, scope definition, and labor productivity were perceived as less critical in comparison. The findings provide a data-driven basis for improving project control strategies, offering valuable insights for construction managers, policymakers, and urban developers to enhance efficiency, minimize risks, and optimize decision-making in Metro Manila’s mid-rise construction sector.

This study sought to understand the experiences of All-but-Dissertation (ABD) students that led them to withdraw from a Doctor of Information Technology (DIT) programme. A total of 27 students from three Philippine universities were interviewed using a semi-structured format. Results show that most participants were driven by extrinsic motivations and viewed graduate education as a pathway to a better life. The challenges they faced were both internal and external in nature (e.g. study-work conflicts and personal problems), which are comparable to those in other disciplines. Most reasons (e.g. limited research experience and dissertation anxiety) for dropping out from this professional doctorate align with findings from studies on Doctor of Philosophy (PhD) programmes. However, two reasons unique to ABD students in the DIT programme were the inclusion of software development and the selection of computing research topics. Overall, these findings provide empirical evidence for addressing issues related to dissertation delays, prolonged doctoral completion times, and attrition in graduate education. Practical and managerial implications derived from this study could inform graduate school policies and practices, with potential applications across other doctoral disciplines.

The Neighborhood Median Pixel Method has previously been introduced as an image processing technique in remote sensing, developed to classify Landsat-8 OLI satellite image pixels into categories of vegetation, water, and built-up areas. This method relies on a lookup table based on the median pixel values within a pixel’s neighborhood and a scoring system that assigns point values for classification. While a 9x9 neighborhood size was originally proposed, a succeeding study suggested a 13x13 neighborhood for better classification accuracy. This study focuses on refining the scoring system used in the Neighborhood Median Pixel Method, particularly the original set of arbitrary point values 14, 4, and 1. Particle Swarm Optimization was employed to systematically explore and optimize these point values, iteratively seeking an ideal configuration for the scoring system. After optimization, the Neighborhood Median Pixel Method exhibited a slight increase in overall accuracy. Using the 9x9 neighborhood size, the accuracy rose from 94% to 94.75%, while with the 13x13 neighborhood size, the accuracy improved from 95.75% to 96%. Furthermore, results indicate that varying point value configurations yield similar classification outcomes, suggesting that the method’s scoring system is robust across multiple configurations and that the originally proposed point value set remains adequate for effective classification.

This research addresses the challenges faced by community welfare services in the Philippines, particularly within the context of healthcare delivery. With the rapid advancement of information technologies, there is a critical need to enhance the efficiency and efficacy of medical information management. The study highlights the issues of delayed access to accurate health information due to disparate systems and poor data quality, which hinder timely medical assistance. It proposes developing a comprehensive e-health solution designed to automate and digitize healthcare records management within barangays—the grassroots level of governance in Philippine society. In terms of the medical resources, allocation, the researcher used Exponential smoothing for forecasting resources ahead of time, reducing the risk of shortages or overstocking. The researcher used Technology Acceptance Model Software Quality Model to assess the system overall capability as perceived by experts and end users. In terms of ISO instrument, all criteria are rated very acceptable in which it emphasizes the system’s overall effectiveness and user-centered design, contributing to a favorable perception of its performance. In terms of the Technology Acceptance Model evaluation, the total mean of 4.03 demonstrates a strong consensus among respondents, highlighting their positive outlook on the system’s usefulness, ease of use, and overall attitude toward its implementation in academic environments.

Artificial intelligence is rapidly reshaping multimedia arts courses by enhancing creative workflows and expanding artistic possibilities. Its integration into creative platforms has allowed students to improve efficiency and explore new techniques, fostering a dynamic digital art environment. However, concerns persist regarding originality, artistic authenticity, and the potential decline of traditional skills. Using the Expectation Confirmation Model, this study examines students’ perceptions of AI in multimedia arts. While students acknowledge its convenience and practical benefits, they remain cautious about its long-term impact on artistic development. An N-gram analysis of student feedback reveals a spectrum of opinions ranging from enthusiasm for technological advancements to ethical concerns about copyright, fair attribution, and the future of handcrafted art. Notably, despite recognizing AI’s advantages, students express reservations about its role in shaping creative expression. These findings highlight the need for educational frameworks that balance AI-driven innovation with the preservation of traditional artistic techniques. Future research should explore on how Philippine institutions can integrate AI with traditional arts while educating students on ethics and industry practices to ensure responsible and competitive creative careers.

Generative AI tools are increasingly transforming multimedia arts courses by streamlining creative processes and expanding the possibilities for artistic expression. The integration of these tools into creative platforms has enabled students to enhance their productivity and experiment with innovative techniques, fostering an environment where digital artistry can thrive. Despite these benefits, the use of AI raises significant concerns regarding originality, artistic authenticity, and the potential erosion of traditional skills. Analysis using the technology acceptance model (TAM) reveals that students broadly accept these tools due to their perceived utility and ease of use, yet they remain cautious about their long-term impact on creative development. A word cloud analysis of student feedback illustrates a diverse array of sentiments, combining enthusiasm for the technological advancements with caution and ethical deliberation about the role of AI in art. Sentiment analysis further indicates an overall optimistic view toward the integration of generative AI, while also uncovering persistent concerns about ethical issues, such as copyright and the fair attribution of creative work. These findings highlight the critical need for educational strategies that balance the benefits of AI-driven efficiency and innovation with the preservation of traditional artistic practices. Future research should explore methods to integrate AI into multimedia arts curricula in a way that augments human creativity and safeguards the unique qualities of handcrafted art, ensuring that technological progress enhances rather than compromises artistic integrity.

In today’s society, technology has become an integral part of daily life. At home, families now have access to the Internet and electronic devices. Although digitalization offers numerous advantages, it also poses problems for young children who can be constantly exposed to online content, which can negatively affect their holistic development. Parents and guardians who are accustomed to traditional parenting can also experience challenges in handling technology at home. The study aims to educate and guide parents and guardians on how to manage their children’s digital activities effectively. Through a hybrid web series and a social media campaign, they can be encouraged to foster a conducive digital environment to better support their children’s early development. Children can also help them build a positive relationship with their children at home. Formative and summative evaluation were conducted to gather information on academic professionals and industry experts. Also included is a test screening to understand the impact of the project on the target audience. With a grand mean of 4.47, the study was able to increase the learning outcomes of the parents and guardians. This can contribute to the existing body of knowledge on digital parenting, child development, and digital media.