This study assesses the potential of advanced materials, specifically graphene, perovskites, and nanostructured ceramics to enhance the energy efficiency, durability, and environmental resilience of 5G and 6G communication systems deployed in harsh environments. A comparative evaluation was conducted based on electrical conductivity, thermal stability, mechanical strength, optical performance, and corrosion resistance, drawing on recent experimental data and life-cycle analyses. Graphene demonstrates electrical conductivity near 10^8 S/m and thermal conductivity up to 5000 W/m-K, enabling transistors with 200 times higher speeds and coatings reducing corrosion by over 90%. Perovskite-based devices achieve solar cell efficiencies up to 34% and optical modulators operating at 170 Gbps. Nanostructured ceramics offer low dielectric loss and stability above 1000°C, supporting high-frequency operation in challenging conditions. Integrating these materials is projected to extend device lifespans by up to 40% and reduce energy and cooling demands by 30%. These findings indicate that adopting advanced materials can significantly improve the performance and sustainability of next-generation communication infrastructure.

Climate change is intensifying maritime risks, including sea level rise, stronger storms, and disrupted ocean currents—posing threats to coastal infrastructure, navigation, and security. Traditional monitoring systems lack the predictive capabilities and coverage to address these evolving challenges. This paper explores the integration of Artificial Intelligence (AI) and satellite-based Earth observation as a next-generation early warning system (EWS) for maritime security. We assess observed and projected hazard trends, propose an AI-enhanced system architecture, and evaluate readiness in climate-vulnerable geographies. The findings highlight actionable strategies to improve forecasting, risk detection, and climate resilience in coastal and maritime domains.

Blue carbon ecosystems (mangroves, saltmarshes, seagrasses) are globally significant carbon sinks, absorbing an estimated 50% of oceanic carbon despite covering only 2% of the ocean surface. However, these important habitats face rapid degradation, with 25% to 50% loss over the past 50–70 years, transforming them into carbon sources. This paper presents a climate-smart communication and sensing framework for real-time monitoring of these critical marine ecosystems. It integrates Artificial Intelligence (AI) with advanced sensing technologies, including satellite, Uncrewed Aerial Vehicles (UAVs), LiDAR, and in-situ sensors, for comprehensive data acquisition and analysis. The framework evaluates energy-efficient and secure underwater (acoustic, optical) and overwater (satellite, cellular, LoRaWAN) communication strategies to ensure continuous data flow. AI algorithms enhance data processing, pattern recognition, predictive modeling, and autonomous operations of platforms like Autonomous Underwater Vehicles (AUVs). This integrated approach not only supports accurate carbon accounting but also yields substantial co-benefits for climate change mitigation and adaptation, biodiversity conservation, and maritime security by deterring illegal activities and pollution. The framework provides a transformative pathway for sustainable blue economy and resilient coastal communities.

This study proposes a Blockchain-Integrated Circular Economy Framework to improve lifecycle tracking and emissions reporting for maritime ICT and energy materials. The system combines Digital Product Passports, IoT telemetry, and smart contracts on a permissioned blockchain to record operational data and end-of-life events for batteries, photovoltaic modules, and navigation equipment. A parametric algorithm calculates emissions by combining production impacts, usage profiles, and recycling credits, while automated incentives promote material recovery. Synthetic data simulations illustrate the framework's ability to monitor degradation, quantify emissions, and enforce circularity incentives. Results indicate that production emissions dominate lifecycle impacts, highlighting the value of integrated tracking and verified recovery to support low-carbon maritime operations.

Accurate resource yield prediction is critical for military logistics, planning, and operational readiness, yet remains challenging due to numerous influencing factors such as environmental conditions, resource quality, and logistical constraints. This study examines the effectiveness of quantile-based data binning on classical machine learning algorithms in predicting resource yields pertinent to military applications. Furthermore, the effectiveness of Backpropagation Artificial Neural Networks (BP-ANN) and Naive Bayes classifiers with regression models such as K-Nearest Neighbors (KNN), Linear Regression, and Multi-Layer Perceptron Regressors (MLPRegressor) are compared using a robust dataset representative of global resource metrics. The results indicate that binning continuous data into quartiles substantially enhances model accuracy, precision, recall, and computational efficiency. In particular, the binned data enables the BP-ANN to achieve an accuracy of approximately 90.4%, with regression models such as KNN and MLPRegressor outperforming this benchmark by attaining accuracies exceeding 93%. Additionally, binning drastically reduced hyperparameter tuning duration from around 149 minutes to less than 10 minutes, underscoring its computational efficiency advantage. Overall, this research demonstrates that quantile-based data binning is a valuable preprocessing technique that improves predictive accuracy, reduces computational cost, and enhances the reliability of classical machine learning models for military resource forecasting.

In military and expeditionary (maritime) health care environments where isolation, safety, limited personnel, and resource constraints can threaten the delivery of frontline health care, access to timely and knowledgeable medical assistance can be extremely valuable. The purpose of this paper is to investigate the use of large language models (LLMs) in military and maritime health care environments by creating an AI-powered, Over-the-Counter (OTC) Medication Assistance Bot using the Mistral-7B model. The bot is intended to be deployed within tactical, or even shipboard systems, and it would empower autonomous, in-the-moment recommendations of medications for specific symptoms, while mitigating the risks associated with deploying personnel self-medicating through potential non-fundamental use errors. For this work, we employed Low-Rank Adaptation (LoRA) to fine-tune the system, and the bot was trained on a specific dataset derived from material on pharmacological sources, contextualized for medical practices in the Philippines. Based on evaluation the model achieved an average F1-score of 0.7296, which is above the 0.60-0.70 expected levels of performance for medical dialogue systems. The research shows promise for the model as it enhances combat and maritime healthcare readiness by providing consistent, low-bandwidth, and local medical assistance when connected medical supervision may not be immediately available.

This study aimed to develop models for predicting trip frequency in San Jose City, Province of Nueva Ecija, Philippines incorporating socio-demographic factors (SDF) and attitudinal factors (AF) through the use of artificial neural network (ANN). Socio-demographic factors in the model include age, sex, civil status (CS), number of children (NOC), barangay, number of household members (NHM), educational attainment (EA), employment status (ES), household income (HI), number of driver license holder (DLH), number of personal vehicles owned (PVO), and number of vehicles owned by the household (VOH) while the attitudinal factors in the model include car dependency (CD), convenience, speed, privacy and safety (PS), health and environment (HE), cost, and comfort. The collected data were processed to develop ANN model in different pandemic stages with 19-19-1 (input-hidden-output) network structure used for these models. The sensitivity analysis (SA) results indicate that in the pre-pandemic period, employment status is the most influential parameter (MIP) to the trip frequency in the study area, while the educational attainment is the MIP during the pandemic period and in the post-pandemic period. The findings of the study signify the effectiveness of ANN in forecasting trip frequency as evident to the low mean absolute percentage error (MAPE) values obtained for the three models. The results can be used by policymakers in making informed strategies in further improving the travel experience of the population in the study area.

The inclusion of Artificial Intelligence (AI) and Machine Learning (ML) into cybersecurity education offers a significant opportunity to customize learning, improve engagement, and connect theoretical concepts with practical applications. This study presents the Dynamic Integration and Optimization of NetCyber Activities (DIONA) system which is an AI-enhanced educational tool developed from the NetFusion Learning Academy (NLA) to tackle ongoing issues in conventional cybersecurity education, including restricted adaptability, absence of real-time feedback, and inadequate practical skill application. The study employs a systematic methodology based on four distinct objectives: (1) to assess the efficacy of NLA in improving student learning across five critical domains— knowledge retention, practical skills, engagement, conceptual understanding, and problem-solving; (2) to examine student and faculty perceptions of its educational value; (3) to develop the ACTIVE AI Framework for AI-driven pedagogy; and (4) to create and validate DIONA as an AI/ML-based experiential learning platform. Statistical and thematic analyses indicated that although NLA effectively enhances knowledge and engagement, deficiencies persist in practical skills and problem-solving, necessitating the incorporation of AI-powered tools. The ACTIVE AI Framework and DIONA system offer customized learning trajectories, AI-generated feedback, and immersive simulations that correspond with authentic cybersecurity challenges. Results endorse the significance of intelligent learning analytics and specialized AI systems in transforming technical education and equipping students for changing digital environments.

This pilot study investigates the adoption trends and challenges of artificial intelligence (AI) and machine learning (ML) in the telecommunications sector, focusing on network analytics and security. Using a two-part design—literature review and survey-based statistical analysis—the study provides early insights into industry perceptions and readiness. With data from 31 telecom professionals in a leading Philippine service provider, results reveal a low level of current implementation but high expectations for future adoption. Despite the small sample size, which limits generalizability, this study contributes a foundational analysis of key drivers and obstacles of AI/ML adoption. It highlights the importance of workforce upskilling, integration strategies, and stakeholder engagement in supporting AI readiness within telecom organizations.

This study examines how multimedia arts students perceive generative artificial intelligence (AI) as a potential threat to traditional artistic practices. While AI-powered tools are increasingly integrated into creative processes, offering benefits such as efficiency, expanded techniques, and access to new artistic possibilities, their use has sparked concerns about the declining role of manual skills and the authenticity of AI-generated works. Using Rogers’ Diffusion of Innovations Theory as a framework, this research explores student insights through qualitative analysis, including keyword frequency and word cloud visualization. Results indicate a generally positive attitude toward AI’s practical applications, yet students express reservations about its impact on creative identity, originality, and ethical considerations such as copyright and attribution. These findings suggest a need for educational approaches that introduce AI critically and constructively, ensuring that emerging technologies enhance rather than replace human creativity. Future research should explore pedagogical strategies that preserve traditional artistic values while preparing students to navigate an evolving creative landscape.