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.

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.

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.

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.

This volume, Mental Health Challenges in Academia: Stressors Faced by Students and Faculty, bravely confronts the issues that many in higher education endure but few openly discuss. From the strains of balancing teaching, research, and administrative duties to the financial pressures, cultural challenges, and emotional burdens faced by students, it brings together a diverse range of perspectives to paint a holistic picture of academic life. It highlights both the systemic issues and the deep personal stories that reveal how intertwined our professional achievements are with our personal well- being. In doing so, it not only informs but also reassures its readers: you are not alone, and there are ways forward.

Since teaching is the foundation of education, program accreditation and institutional performance are directly correlated with its effectiveness. By creating a competitive and supportive learning environment, faculty performance has a direct impact on an academic institution’s ability to fulfill its vision and goal. To provide a thorough and impartial assessment of teaching performance, this study uses data mining algorithms to extract insightful information about the elements that go into good instruction, including both structured and unstructured data. This is done in response to the urgent need for faculty performance evaluation. To help institutions identify their strengths, rectify their flaws, and encourage ongoing growth in their teaching and learning processes, the system was created. Looking for trends in teacher data. Furthermore, sentiment analysis methods are employed to assess qualitative input, and Laravel 8.0 provides the framework for putting these algorithms into practice. A grand mean score of 4.38, which is considered “Very Acceptable,” was obtained from expert evaluations of the system, demonstrating its dependability and efficacy in assisting with faculty performance reviews.

Floods are among the most devastating natural hazards, causing widespread damage to lives, livelihoods, and infrastructure, particularly in vulnerable river basins. The Cagayan River Basin (CRB), the largest and most flood-prone basin in the Philippines, remains a significant challenge for disaster risk management. This study developed an event-based hydrologic–hydraulic modeling framework by coupling HEC-HMS rainfall–runoff simulations with HEC-RAS 2D unsteady flow routing to produce validated flood hazard maps. Inputs included rainfall from 41 gauge stations and observed inflows from the Magat Dam, processed in HEC-DSS. Validation utilized 137 surveyed flood marks collected from post-flood surveys, community reports, government archives, and household RTK measurements, with a concentration in Tuguegarao City. The coupled model reproduced key hydrograph peaks with moderate accuracy (R2 = 0.56, Bias = +0.32 m, RMSE = 1.61 m, MAE = 1.43 m), although NSE (−2.30) reflected the limits of daily rainfall inputs. Simulated hazard maps identified 767.97 km2 of inundated area (approximately 2.77% of CRB), concentrated along the floodplain and at the Magat confluence. Unlike previous scenario-based or localized efforts, this study delivers the first basin-wide, event-validated flood hazard maps for the CRB using integrated depth and depth–velocity criteria. The resulting hazard layers provide a scientific basis for strengthening evacuation planning, guiding land-use and infrastructure decisions, and supporting long-term resilience strategies in one of the Philippines’ most flood-prone rivers.

This book offers a comprehensive guide to navigating the transformation of the workforce due to the influence of artificial intelligence (AI) across industries and discusses detailed strategies for executing reskilling and upskilling programs for professionals and managers in charge of workforce development, training, and employee retention in an AI-driven landscape. As AI continues to reshape sectors and redefine job roles, the need for a strategy for an adaptable and well-equipped workforce has never been more critical. By analyzing AI’s integration with other emerging technologies, such as blockchain and IoT, and their specific impact across sectors, this book prepares readers to meet the unique demands of an AI-driven workforce transformation.

This chapter establishes the core foundations of upskilling and reskilling in an AI-driven workplace and discusses their importance in workforce innovation. Ongoing education and continuous learning are highlighted as key factors in making workers responsive to changing industry needs. Through the integration of industry and educational institution case study insights, such as Siemens lifelong learning initiatives, the Center for Integrated STEM Education–Massachusetts Institute of Technology Responsible AI for Social Empowerment and Education (CISTEM-MIT RAISE) AI literacy initiatives, and the Miriam College Technology Business Incubator (MC-TBI), the discussion emphasizes how workforce strategies powered by AI improve skills development, increase productivity, and establish sustainable career paths. Predictive workforce analytics, adaptive learning pathways, and industry-specific training methodologies are examined as high-impact interventions for mitigating skill deficiencies. The transforming character of learning institutions and training schemes is also considered, with special emphasis placed on the necessity for academia–industry–government partnerships for the creation of scalable and accessible learning ecosystems. The findings reveal that AI-supported reskilling initiatives, backed by established assessment methodologies and policy frameworks, significantly improve workforce adaptability and long-term employment prospects, promoting ongoing learning environments, accessibility-focused training solutions, and collaborative partnerships to future-proof workforce in the digital economy.

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.